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Merge pull request #307 from Hestia-Homes/eon

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Eon
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KhalimCK 2024-06-25 15:57:05 +01:00 • committed by GitHub
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35 changed files with 4563 additions and 545 deletions
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2
.idea/Model.iml generated
View file

@ -7,7 +7,7 @@
<sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
<sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
</content>
<orderEntry type="jdk" jdkName="Python 3.10 (model_data)" jdkType="Python SDK" />
<orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="PyNamespacePackagesService">

2
.idea/misc.xml generated
View file

@ -3,7 +3,7 @@
<component name="Black">
<option name="sdkName" value="Python 3.10 (backend)" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (model_data)" project-jdk-type="Python SDK" />
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
<component name="PythonCompatibilityInspectionAdvertiser">
<option name="version" value="3" />
</component>

42
backend/OrdnanceSurvey.py
View file

@ -38,7 +38,11 @@ class OrdnanceSuveyClient:
raise ValueError("No results found - run get_places_api first")
self.address_os = self.most_relevant_result["ADDRESS"]
self.postcode_os = self.most_relevant_result["POSTCODE"]
if "POSTCODE" in self.most_relevant_result:
self.postcode_os = self.most_relevant_result["POSTCODE"]
else:
self.postcode_os = self.most_relevant_result["POSTCODE_LOCATOR"]
# We strip out the postcode from the address as this is already stored separately
self.address_os = self.address_os.replace(self.postcode_os, "").strip()
# Remove trailing comma
@ -49,7 +53,7 @@ class OrdnanceSuveyClient:
self.postcode_os = self.postcode_os.upper()
@lru_cache(maxsize=128)
def get_places_api(self):
def get_places_api(self, filter_by_postcode=False):
"""
This method is tasked with getting the places api from the Ordnance Survey.
"""
@ -58,16 +62,35 @@ class OrdnanceSuveyClient:
raise ValueError("Ordnance Survey API key not specified")
encoded_address_query = urllib.parse.quote(self.full_address)
url = (f"https://api.os.uk/search/places/v1/find?query={encoded_address_query}&key="
f"{self.api_key}")
url = (
f"https://api.os.uk/search/places/v1/find?query={encoded_address_query}&dataset=DPA,LPI&matchprecision=10"
f"&key={self.api_key}"
)
response = requests.get(url)
if response.status_code == 200:
data = response.json()
results = data['results']
res = data["results"]
if filter_by_postcode:
results = []
for r in res:
if "DPA" in r:
if r["DPA"]["POSTCODE"] == self.postcode:
results.append(r)
elif "LPI" in r:
if r["LPI"]["POSTCODE_LOCATOR"] == self.postcode:
results.append(r)
else:
raise ValueError("Could not find postcode in either DPA or LPI")
else:
results = res
self.results = results
# Extract some details about the best match
self.most_relevant_result = self.results[0]["DPA"]
self.most_relevant_result = self.results[0]["DPA"] if "DPA" in self.results[0] else self.results[0]["LPI"]
self.parse_classification_code(self.most_relevant_result["CLASSIFICATION_CODE"])
self.set_places_address()
@ -94,11 +117,14 @@ class OrdnanceSuveyClient:
value_map = {
# In the OS api, "RD" is a "Dwelling" however this is not valid property type in the EPC database
'RD': {},
'RD02': {'property_type': 'House', 'built_form': 'Detatched'},
'RD03': {'property_type': 'House', 'built_form': 'Semi-Detatched'},
'RD02': {'property_type': 'House', 'built_form': 'Detached'},
'RD03': {'property_type': 'House', 'built_form': 'Semi-Detached'},
'RD04': {'property_type': 'House', 'built_form': 'Mid-Terrace'},
'RD06': {'property_type': 'Flat'},
}
# Other classifications can be found in here:
# https://osdatahub.os.uk/docs/places/technicalSpecification in the CLASSIFICATION_CODE description.
# A lookup table csv can be downloaded which contains all of the codes
mapped = value_map.get(classification_code, {})
self.property_type = mapped.get("property_type", "")

109
backend/Property.py
View file

@ -18,6 +18,7 @@ from recommendations.recommendation_utils import (
esimtate_pitched_roof_area,
estimate_windows,
)
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
ENVIRONMENT = os.environ.get("ENVIRONMENT", "dev")
DATA_BUCKET = os.environ.get(
@ -93,7 +94,10 @@ class Property:
non_invasive_recommendations else []
)
# This is a list of measures that have been recommended for the property
self.measures = ast.literal_eval(measures) if measures else None
if isinstance(measures, list):
self.measures = measures
else:
self.measures = ast.literal_eval(measures) if measures else None
self.uprn = epc_record.get("uprn")
self.full_sap_epc = epc_record.get("full_sap_epc")
@ -159,6 +163,9 @@ class Property:
self.current_energy_bill = None
self.expected_energy_bill = None
self.heating_energy_source = None
self.hot_water_energy_source = None
self.recommendations_scoring_data = []
self.parse_kwargs(kwargs)
@ -200,11 +207,11 @@ class Property:
# difference_record = self.epc_record - self.epc_record
# TODO: change these lower and replace in the settings file
print(
"CHANGE THE LATEST FIELD TO REMOVE NUMBER HABITABLE ROOMS IF WE WANT TO USE STARTING/ENDING"
)
# print(
# "CHANGE THE LATEST FIELD TO REMOVE NUMBER HABITABLE ROOMS IF WE WANT TO USE STARTING/ENDING"
# )
fixed_data_col_names = MANDATORY_FIXED_FEATURES + LATEST_FIELD
print("NEED TO CHANGE THE DASH TO LOWER CASE")
# print("NEED TO CHANGE THE DASH TO LOWER CASE")
fixed_data_col_names = [
x.lower().replace("_", "-") for x in fixed_data_col_names
]
@ -582,6 +589,26 @@ class Property:
floor_area_decile_thresholds=floor_area_decile_thresholds,
)
self.set_energy_source()
self.find_energy_sources()
self.set_current_energy_bill()
def set_current_energy_bill(self):
"""
Given what we know about the property now, estimates the current energy consumption using the UCL paper
https://www.sciencedirect.com/science/article/pii/S0378778823002542
:return:
"""
starting_heat_demand = (
float(self.data["energy-consumption-current"]) * self.floor_area
)
self.current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
epc_energy_consumption=starting_heat_demand,
current_epc_rating=self.data["current-energy-rating"],
total_floor_area=self.floor_area
)
self.current_energy_bill = AnnualBillSavings.calculate_annual_bill(self.current_adjusted_energy)
def set_spatial(self, spatial: pd.DataFrame):
"""
@ -844,8 +871,8 @@ class Property:
# where a property is marked as being on the first floor
if self.floor_level > 0:
# We check if there is another property below
if not self.floor["another_property_below"]:
# We check if there is another property below (for a non-sap assessment)
if not self.floor["another_property_below"] and self.floor["thermal_transmittance_unit"] is None:
self.floor_level = 0
return
@ -902,14 +929,13 @@ class Property:
return component_data
def set_adjusted_energy(
self, current_adjusted_energy, expected_adjusted_energy, current_energy_bill, expected_energy_bill
self, expected_adjusted_energy, expected_energy_bill
):
"""
Stores these values for usage later
"""
self.current_adjusted_energy = current_adjusted_energy
self.expected_adjusted_energy = expected_adjusted_energy
self.current_energy_bill = current_energy_bill
self.expected_energy_bill = expected_energy_bill
def set_windows_count(self):
@ -990,3 +1016,66 @@ class Property:
# Set the energy source based on the conditions above
self.energy_source = energy_source
def find_energy_sources(self):
# Based on the heating and the hot water
heating_fuel_mapping = {
'has_mains_gas': 'Natural Gas',
'has_electric': 'Electricity',
'has_oil': 'Oil',
'has_wood_logs': 'Wood Logs',
'has_coal': 'Coal',
'has_anthracite': 'Anthracite',
'has_smokeless_fuel': 'Smokeless Fuel',
'has_lpg': 'LPG',
'has_b30k': 'B30K Biofuel',
'has_air_source_heat_pump': 'Electricity',
'has_ground_source_heat_pump': 'Electricity',
'has_water_source_heat_pump': 'Electricity',
'has_electric_heat_pump': 'Electricity',
'has_solar_assisted_heat_pump': 'Electricity',
'has_exhaust_source_heat_pump': 'Electricity',
'has_community_heat_pump': 'Electricity',
'has_wood_pellets': 'Wood Pellets',
'has_community_scheme': 'Varied (Community Scheme)'
}
# Hot water
heater_type_to_fuel = {
'gas instantaneous': 'Natural Gas',
'electric heat pump': 'Electricity',
'electric immersion': 'Electricity',
'gas boiler': 'Natural Gas',
'oil boiler': 'Oil',
'electric instantaneous': 'Electricity',
'gas multipoint': 'Natural Gas',
'heat pump': 'Electricity',
'solid fuel boiler': 'Solid Fuel',
'solid fuel range cooker': 'Solid Fuel',
'room heaters': 'Varied' # Could be any fuel, further specifics needed based on context
}
# Define a mapping from system types to general categories or modifications of fuel types
system_type_modification = {
'from main system': 'Main System',
'from secondary system': 'Secondary System',
'from second main heating system': 'Secondary System',
'community scheme': 'Community Scheme'
}
self.heating_energy_source = [
fuel for key, fuel in heating_fuel_mapping.items() if self.main_heating.get(key, False)
]
if len(self.heating_energy_source) == 0 or len(self.heating_energy_source) > 1:
raise Exception("Investigate em")
self.heating_energy_source = self.heating_energy_source[0]
if self.hotwater["heater_type"] is not None:
self.hot_water_energy_source = heater_type_to_fuel[self.hotwater["heater_type"]]
else:
fuel = system_type_modification[self.hotwater["system_type"]]
if fuel == 'Main System':
self.hot_water_energy_source = self.heating_energy_source
else:
raise Exception("Investiage me")

46
backend/SearchEpc.py
View file

@ -11,6 +11,7 @@ from BaseUtility import Definitions
from utils.logger import setup_logger
from typing import List
from fuzzywuzzy import process
from backend.app.utils import sap_to_epc
logger = setup_logger()
@ -190,15 +191,15 @@ class SearchEpc:
self.property_type = property_type
self.fast = fast
@classmethod
def get_house_number(cls, address: str) -> str | None:
@staticmethod
def get_house_number(address: str, postcode=None) -> str | None:
"""
This method uses the usaddress library to parse an address and extract the primary house or flat number.
"""
try:
# Custom regex to catch a broad range of cases
pattern = r'(?i)(?:flat|apartment)\s*(\d+)|^\s*(\d+)'
try:
# Updated regex to catch house numbers including alphanumeric ones
pattern = r'(?i)(?:flat|apartment)\s*(\d+\w*)|^\s*(\d+\w*)'
match = re.search(pattern, address)
if match:
return next(g for g in match.groups() if g is not None)
@ -207,6 +208,11 @@ class SearchEpc:
# First, try to get the 'OccupancyIdentifier' if 'OccupancyType' is detected
for part, type_ in parsed:
if type_ == 'OccupancyIdentifier':
if postcode is not None:
if part == postcode.split(" ")[0]:
continue
if part == postcode.split(" ")[1]:
continue
return part # This assumes the first 'OccupancyIdentifier' after 'OccupancyType' is the primary
# number
@ -216,7 +222,7 @@ class SearchEpc:
return address_number.replace(",", "") # Remove any trailing commas
except Exception as e:
print(f"Error parsing address: {e}")
raise Exception(f"Error parsing address: {e}")
return None
@ -428,7 +434,8 @@ class SearchEpc:
self, initial_postcode: str,
lmks_to_drop: list[str] | None = None,
built_form: str = "",
property_type: str = ""
property_type: str = "",
exclude_old: bool = False
):
"""
Fetches and processes EPC data for a given initial postcode, applying successive trimming
@ -447,6 +454,7 @@ class SearchEpc:
:param lmks_to_drop: List of 'lmk-key' values to be excluded from the EPC data.
:param built_form: The 'built-form' value to be used for filtering the EPC data.
:param property_type: The 'property-type' value to be used for filtering the EPC data.
:param exclude_old: Flag to exclude EPC data older than 10 years.
:return:
"""
@ -474,9 +482,23 @@ class SearchEpc:
if lmks_to_drop is not None:
epc_data = epc_data[~epc_data["lmk-key"].isin(lmks_to_drop)]
try:
epc_data['lodgement-datetime'] = pd.to_datetime(
epc_data['lodgement-datetime'], format='%Y-%m-%d %H:%M:%S', errors='coerce'
)
except Exception as e:
logger.error("Problem formatting lodgement-datime, appling fallback: " + str(e))
epc_data['lodgement-datetime'] = pd.to_datetime(epc_data['lodgement-datetime'], errors='coerce')
if exclude_old:
# Exclude EPC data older than 10 years
epc_data = epc_data[
epc_data["lodgement-datetime"] > (pd.Timestamp.now() - pd.DateOffset(years=10))
]
if not epc_data.empty:
# Further processing of the EPC data
epc_data['lodgement-datetime'] = pd.to_datetime(epc_data['lodgement-datetime'], errors='coerce')
epc_data = epc_data.sort_values("lodgement-datetime", ascending=False).groupby("uprn").head(1)
epc_data["house_number"] = epc_data["address"].apply(lambda add1: self.get_house_number(add1))
epc_data["numeric_house_number"] = epc_data["house_number"].apply(
@ -554,7 +576,7 @@ class SearchEpc:
# If loop finishes without a valid response, raise an exception
raise Exception("Unable to find postcode data after trimming - investigate me")
def estimate_epc(self, property_type, built_form, lmks_to_drop=None):
def estimate_epc(self, property_type, built_form, lmks_to_drop=None, exclude_old=False):
"""
For a property that does not have an EPC, we retrieve the EPC data for the closest properties
and estimate the EPC for the property in question.
@ -567,6 +589,7 @@ class SearchEpc:
the ordnance survey api
:param lmks_to_drop: This is a list of LMK keys that should be dropped from the estimation process. This
is used as an override for testing, to drop EPCs for the property we are testing
:param exclude_old: Used to drop any expired EPCs (more than 10 years old)
:return:
"""
@ -576,7 +599,8 @@ class SearchEpc:
initial_postcode=self.postcode,
lmks_to_drop=lmks_to_drop,
built_form=built_form,
property_type=property_type
property_type=property_type,
exclude_old=exclude_old
)
# If we have missing lodgment date, we fill it with inspection-date
@ -624,6 +648,8 @@ class SearchEpc:
else:
estimated_epc["lodgement-date"] = estimated_epc["lodgement-datetime"].strftime("%Y-%m-%d")
estimated_epc["current-energy-rating"] = sap_to_epc(estimated_epc["current-energy-efficiency"])
estimated_epc["postcode"] = self.postcode
estimated_epc["uprn"] = self.uprn
estimated_epc["address"] = self.full_address

623
backend/apis/GoogleSolarApi.py
View file

@ -1,336 +1,329 @@
from backend.Property import Property
from backend.SearchEpc import SearchEpc
from etl.epc.Record import EPCRecord
from dotenv import load_dotenv
from utils.s3 import read_dataframe_from_s3_parquet
import os
import pandas as pd
import numpy as np
from recommendations.Costs import MCS_SOLAR_PV_COST_DATA
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
import requests
from functools import lru_cache
import time
load_dotenv(dotenv_path="backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
# This is for 6 Laura Close, Tintagel, PL34 0EB (same property that Cotswolrd energy used)
uprn = 100040099104
# This is for 353A, Hermitage Lane, ME16 9NT (one of the e.on properties)
uprn = 200000964454
class GoogleSolarApi:
NORTH_FACING_AZIMUTH_RANGE = (-30, 30)
cleaning_data = read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
)
# Conservative estimate of the proportion of electricity that will be consumed, whereas the rest will
# be exported
SOLAR_CONSUMPTION_PROPORTION = 0.5
searcher = SearchEpc(address1="", postcode="", uprn=uprn, auth_token=EPC_AUTH_TOKEN, os_api_key="")
# These are variables, described in the documentation for cost analysis for non-us locations, seen here
# https://developers.google.com/maps/documentation/solar/calculate-costs-non-us
# We use the default figures that the API uses for US locations
searcher.find_property(skip_os=True)
# The factor by which the cost of electricity increases annually. The Solar API uses 1.022 (2.2% annual increase)
# for US locations.
cost_increase_factor = 1.022
epc_records = {
'original_epc': searcher.newest_epc.copy(),
'full_sap_epc': searcher.full_sap_epc.copy(),
'old_data': searcher.older_epcs.copy(),
}
# The efficiency at which an inverter converts the DC electricity that is produced by the solar panels to the AC
# electricity that is used in a household. The Solar API uses 85% for US locations. We use 0.95.5 which is the
# middle value of the 93-98% range, cited by Sunsave:
# https://www.sunsave.energy/solar-panels-advice/system-size/inverters
dc_to_ac_rate = 0.955
epc = EPCRecord(
epc_records=epc_records,
run_mode="newdata",
cleaning_data=cleaning_data
)
# The Solar API uses 1.04 (4% annual increase) for US locations
discount_rate = 1.04
uprn_filenames = read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev", file_key="spatial/filename_meta.parquet"
)
# How much the efficiency of the solar panels declines each year. The Solar API uses 0.995 (0.5% annual decrease)
# for US locations
efficiency_depreciation_factor = 0.995
p = Property(
id=0,
address=searcher.address_clean,
postcode=searcher.postcode_clean,
epc_record=epc,
already_installed={},
non_invasive_recommendations={},
)
# The expected lifespan of the solar installation. The Solar API uses 20 years. Adjust this value as needed for
# your area
installation_life_span = 20
p.get_spatial_data(uprn_filenames)
def __init__(self, api_key, max_retries=5):
"""
Initialize the GoogleSolarApi class with the provided API key and maximum retries.
longitude = p.spatial["longitude"]
latitude = p.spatial["latitude"]
:param api_key: The API key to authenticate requests to the Google Solar API.
:param max_retries: The maximum number of retries for the API request (default is 5).
"""
self.api_key = api_key
self.max_retries = max_retries
self.base_url = "https://solar.googleapis.com/v1"
api_key = "AIzaSyCIz8Psu5h-1txuDX0rQpUTgkvdj8yohqU"
url = 'https://solar.googleapis.com/v1/solarPotential'
params = {
'location.latitude': f'{latitude:.5f}',
'location.longitude': f'{longitude:.5f}',
'requiredQuality': "MEDIUM",
'key': api_key
}
self.insights_data = None
self.roof_segments = []
insights_url = 'https://solar.googleapis.com/v1/buildingInsights:findClosest'
# property attributes:
self.floor_area = None
self.roof_area = None
self.roof_segment_indexes = None
self.panel_area = None
self.panel_wattage = None
self.panel_performance = None
# Make the GET request to the Solar API
insights_response = requests.get(insights_url, params=params)
insights_data = insights_response.json()
def get_building_insights(self, longitude, latitude, required_quality="MEDIUM", max_retries=None):
"""
Make an API request to retrieve building insights based on the given longitude and latitude, with retry
mechanism.
solar_potential = insights_data["solarPotential"]
:param longitude: The longitude of the location.
:param latitude: The latitude of the location.
:param required_quality: The required quality of the data (default is "MEDIUM").
:param max_retries: The maximum number of retries for the API request (default is None, which uses the
instance's max_retries).
:return: The JSON response containing the building insights data.
"""
if max_retries is None:
max_retries = self.max_retries
from pprint import pprint
insights_url = f"{self.base_url}/buildingInsights:findClosest"
params = {
'location.latitude': f'{latitude:.5f}',
'location.longitude': f'{longitude:.5f}',
'requiredQuality': required_quality,
'key': self.api_key
}
pprint(solar_potential)
attempt = 0
while attempt < max_retries:
try:
response = requests.get(insights_url, params=params)
response.raise_for_status() # Raise an error for bad status codes
return response.json()
except requests.exceptions.RequestException as e:
attempt += 1
print(f"Attempt {attempt} failed: {e}")
time.sleep(2 ** attempt) # Exponential backoff
if attempt >= max_retries:
raise
# This is the size of the panels used in the calculation - 400 watt
solar_potential["panelCapacityWatts"]
# Height of the panels used
solar_potential["panelHeightMeters"]
# Width of the panels used
solar_potential["panelWidthMeters"]
@lru_cache(maxsize=128)
def get(self, longitude, latitude, required_quality="MEDIUM"):
"""
Wrapper function that calls get_building_insights and extracts roof segments, with caching.
solar_potential["wholeRoofStats"]
:param longitude: The longitude of the location.
:param latitude: The latitude of the location.
:param required_quality: The required quality of the data (default is "MEDIUM").
:return: The JSON response containing the building insights data.
"""
# Copy of response for testing - 6 Laura Close, Tintagel, PL34 0EB
# {'name': 'buildings/ChIJ2yC6t4KEa0gRh2TIssogI7k', 'center': {'latitude': 50.667375, 'longitude': -4.7416833},
# 'imageryDate': {'year': 2021, 'month': 7, 'day': 19}, 'regionCode': 'GB', 'solarPotential': {'maxArrayPanelsCount':
# 39, 'maxArrayAreaMeters2': 76.578636, 'maxSunshineHoursPerYear': 1172.0627, 'carbonOffsetFactorKgPerMwh':
# 478.99942, 'wholeRoofStats': {'areaMeters2': 129.65686, 'sunshineQuantiles': [537, 738.3836, 805.62445, 842.6802,
# 909.8431, 972.15234, 1036.1013, 1092.051, 1135.8192, 1163.1444, 1193.6012], 'groundAreaMeters2': 112.33},
# 'roofSegmentStats': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'stats': {'areaMeters2': 44.08321,
# 'sunshineQuantiles': [614, 940.86975, 982.39124, 1057.0664, 1109.6869, 1137.5837, 1152.9211, 1163.1106, 1168.2212,
# 1170.8883, 1193.6012], 'groundAreaMeters2': 37.61}, 'center': {'latitude': 50.6673664, 'longitude':
# -4.741714099999999}, 'boundingBox': {'sw': {'latitude': 50.6673354, 'longitude': -4.741777}, 'ne': {'latitude':
# 50.6674029, 'longitude': -4.7416472}}, 'planeHeightAtCenterMeters': 93.0221}, {'pitchDegrees': 34.39779,
# 'azimuthDegrees': 31.74401, 'stats': {'areaMeters2': 44.622986, 'sunshineQuantiles': [537, 671.49774, 733.84985,
# 780.82733, 801.4026, 814.0189, 824.0077, 847.77484, 895.08295, 950.1469, 1123.3503], 'groundAreaMeters2': 36.82},
# 'center': {'latitude': 50.6673966, 'longitude': -4.7416813}, 'boundingBox': {'sw': {'latitude': 50.667361,
# 'longitude': -4.7417497}, 'ne': {'latitude': 50.6674303, 'longitude': -4.741615599999999}},
# 'planeHeightAtCenterMeters': 92.87593}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'stats': {
# 'areaMeters2': 17.074476, 'sunshineQuantiles': [644.71136, 731.0546, 782.89813, 842.7107, 908.55585, 966.6212,
# 1010.6367, 1038.2543, 1053.2788, 1090.6831, 1128.0178], 'groundAreaMeters2': 17.050001}, 'center': {'latitude':
# 50.66740850000001, 'longitude': -4.7416025}, 'boundingBox': {'sw': {'latitude': 50.6673895, 'longitude':
# -4.7416436}, 'ne': {'latitude': 50.667431199999996, 'longitude': -4.7415572}}, 'planeHeightAtCenterMeters':
# 90.630356}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'stats': {'areaMeters2': 13.501617,
# 'sunshineQuantiles': [749, 976.85345, 1059.0062, 1081.6173, 1097.4441, 1110.3171, 1128.2186, 1133.9421, 1142.068,
# 1148.2168, 1157.632], 'groundAreaMeters2': 12.02}, 'center': {'latitude': 50.667315699999996, 'longitude':
# -4.741675400000001}, 'boundingBox': {'sw': {'latitude': 50.667291399999996, 'longitude': -4.7417066},
# 'ne': {'latitude': 50.6673372, 'longitude': -4.741648400000001}}, 'planeHeightAtCenterMeters': 92.36334},
# {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334, 'stats': {'areaMeters2': 10.374564, 'sunshineQuantiles': [
# 617.9507, 752.2504, 847.66315, 872.0505, 881.26227, 900.9639, 933.3188, 967.4747, 1000.8129, 1038.3002, 1105.545],
# 'groundAreaMeters2': 8.83}, 'center': {'latitude': 50.6673295, 'longitude': -4.7417128}, 'boundingBox': {'sw': {
# 'latitude': 50.6673134, 'longitude': -4.7417422}, 'ne': {'latitude': 50.6673413, 'longitude': -4.7416775}},
# 'planeHeightAtCenterMeters': 92.31146}], 'solarPanelConfigs': [{'panelsCount': 4, 'yearlyEnergyDcKwh': 1867.1516,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 4,
# 'yearlyEnergyDcKwh': 1867.1515, 'segmentIndex': 0}]}, {'panelsCount': 5, 'yearlyEnergyDcKwh': 2335.0068,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 5,
# 'yearlyEnergyDcKwh': 2335.0068, 'segmentIndex': 0}]}, {'panelsCount': 6, 'yearlyEnergyDcKwh': 2799.8508,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 6,
# 'yearlyEnergyDcKwh': 2799.8508, 'segmentIndex': 0}]}, {'panelsCount': 7, 'yearlyEnergyDcKwh': 3264.6506,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 7,
# 'yearlyEnergyDcKwh': 3264.6506, 'segmentIndex': 0}]}, {'panelsCount': 8, 'yearlyEnergyDcKwh': 3726.2405,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 8,
# 'yearlyEnergyDcKwh': 3726.2405, 'segmentIndex': 0}]}, {'panelsCount': 9, 'yearlyEnergyDcKwh': 4187.721,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 9,
# 'yearlyEnergyDcKwh': 4187.721, 'segmentIndex': 0}]}, {'panelsCount': 10, 'yearlyEnergyDcKwh': 4646.094,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 10,
# 'yearlyEnergyDcKwh': 4646.094, 'segmentIndex': 0}]}, {'panelsCount': 11, 'yearlyEnergyDcKwh': 5103.777,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 10,
# 'yearlyEnergyDcKwh': 4646.094, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
# 'panelsCount': 1, 'yearlyEnergyDcKwh': 457.68268, 'segmentIndex': 3}]}, {'panelsCount': 12, 'yearlyEnergyDcKwh':
# 5559.845, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 10,
# 'yearlyEnergyDcKwh': 4646.094, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 913.7509, 'segmentIndex': 3}]}, {'panelsCount': 13, 'yearlyEnergyDcKwh':
# 6013.053, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 11,
# 'yearlyEnergyDcKwh': 5099.302, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 913.7509, 'segmentIndex': 3}]}, {'panelsCount': 14, 'yearlyEnergyDcKwh':
# 6461.664, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 12,
# 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 913.7509, 'segmentIndex': 3}]}, {'panelsCount': 15, 'yearlyEnergyDcKwh':
# 6902.33, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 12,
# 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162,
# 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 16, 'yearlyEnergyDcKwh':
# 7321.6436, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 12,
# 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099,
# 'panelsCount': 1, 'yearlyEnergyDcKwh': 419.31348, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees':
# 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 17,
# 'yearlyEnergyDcKwh': 7740.388, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331,
# 'panelsCount': 12, 'yearlyEnergyDcKwh': 5547.9126, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775,
# 'azimuthDegrees': 301.1099, 'panelsCount': 2, 'yearlyEnergyDcKwh': 838.0579, 'segmentIndex': 2}, {'pitchDegrees':
# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]},
# {'panelsCount': 18, 'yearlyEnergyDcKwh': 8154.265, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
# 'azimuthDegrees': 218.25331, 'panelsCount': 13, 'yearlyEnergyDcKwh': 5961.7896, 'segmentIndex': 0},
# {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 2, 'yearlyEnergyDcKwh': 838.0579,
# 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh':
# 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 19, 'yearlyEnergyDcKwh': 8566.032, 'roofSegmentSummaries': [{
# 'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 14, 'yearlyEnergyDcKwh': 6373.556,
# 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 2, 'yearlyEnergyDcKwh':
# 838.0579, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 20, 'yearlyEnergyDcKwh': 8976.624,
# 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 838.0579, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees':
# 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 21,
# 'yearlyEnergyDcKwh': 9380.78, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331,
# 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775,
# 'azimuthDegrees': 301.1099, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1242.214, 'segmentIndex': 2}, {'pitchDegrees':
# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}]},
# {'panelsCount': 22, 'yearlyEnergyDcKwh': 9784.078, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 4, 'yearlyEnergyDcKwh': 1645.5122,
# 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh':
# 1354.4171, 'segmentIndex': 3}]}, {'panelsCount': 23, 'yearlyEnergyDcKwh': 10162.354, 'roofSegmentSummaries': [{
# 'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484,
# 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 4, 'yearlyEnergyDcKwh':
# 1645.5122, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 1, 'yearlyEnergyDcKwh': 378.2754, 'segmentIndex': 4}]}, {'panelsCount': 24, 'yearlyEnergyDcKwh':
# 10535.894, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099,
# 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees':
# 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294,
# 'azimuthDegrees': 308.42334, 'panelsCount': 1, 'yearlyEnergyDcKwh': 378.2754, 'segmentIndex': 4}]}, {'panelsCount':
# 25, 'yearlyEnergyDcKwh': 10901.273, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees':
# 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 3.0681775,
# 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees':
# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3},
# {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497,
# 'segmentIndex': 4}]}, {'panelsCount': 26, 'yearlyEnergyDcKwh': 11242.756, 'roofSegmentSummaries': [{'pitchDegrees':
# 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 1, 'yearlyEnergyDcKwh': 341.4827,
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 27, 'yearlyEnergyDcKwh':
# 11579.401, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 678.1277, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
# {'panelsCount': 28, 'yearlyEnergyDcKwh': 11919.106, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1017.83356,
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 29, 'yearlyEnergyDcKwh':
# 12255.358, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
# 'panelsCount': 4, 'yearlyEnergyDcKwh': 1354.0854, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
# {'panelsCount': 30, 'yearlyEnergyDcKwh': 12586.448, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 5, 'yearlyEnergyDcKwh': 1685.1748,
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 31, 'yearlyEnergyDcKwh':
# 12911.502, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
# 'panelsCount': 6, 'yearlyEnergyDcKwh': 2010.2289, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
# {'panelsCount': 32, 'yearlyEnergyDcKwh': 13233.139, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 7, 'yearlyEnergyDcKwh': 2331.8652,
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 33, 'yearlyEnergyDcKwh':
# 13554.602, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
# 'panelsCount': 8, 'yearlyEnergyDcKwh': 2653.3286, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
# {'panelsCount': 34, 'yearlyEnergyDcKwh': 13893.903, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 9, 'yearlyEnergyDcKwh': 2992.6301,
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 35, 'yearlyEnergyDcKwh':
# 14221.166, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
# 'panelsCount': 10, 'yearlyEnergyDcKwh': 3319.893, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]},
# {'panelsCount': 36, 'yearlyEnergyDcKwh': 14536.154, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022,
# 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 11, 'yearlyEnergyDcKwh': 3634.8809,
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 37, 'yearlyEnergyDcKwh':
# 14850.317, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
# 'panelsCount': 12, 'yearlyEnergyDcKwh': 3949.0444, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775,
# 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees':
# 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3},
# {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497,
# 'segmentIndex': 4}]}, {'panelsCount': 38, 'yearlyEnergyDcKwh': 15160.658, 'roofSegmentSummaries': [{'pitchDegrees':
# 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15, 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0},
# {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401, 'panelsCount': 13, 'yearlyEnergyDcKwh': 4259.385,
# 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees': 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh':
# 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596, 'azimuthDegrees': 132.60162, 'panelsCount': 3,
# 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees': 31.666294, 'azimuthDegrees': 308.42334,
# 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}, {'panelsCount': 39, 'yearlyEnergyDcKwh':
# 15438.986, 'roofSegmentSummaries': [{'pitchDegrees': 31.443022, 'azimuthDegrees': 218.25331, 'panelsCount': 15,
# 'yearlyEnergyDcKwh': 6784.1484, 'segmentIndex': 0}, {'pitchDegrees': 34.39779, 'azimuthDegrees': 31.74401,
# 'panelsCount': 14, 'yearlyEnergyDcKwh': 4537.713, 'segmentIndex': 1}, {'pitchDegrees': 3.0681775, 'azimuthDegrees':
# 301.1099, 'panelsCount': 5, 'yearlyEnergyDcKwh': 2019.0519, 'segmentIndex': 2}, {'pitchDegrees': 27.093596,
# 'azimuthDegrees': 132.60162, 'panelsCount': 3, 'yearlyEnergyDcKwh': 1354.4171, 'segmentIndex': 3}, {'pitchDegrees':
# 31.666294, 'azimuthDegrees': 308.42334, 'panelsCount': 2, 'yearlyEnergyDcKwh': 743.65497, 'segmentIndex': 4}]}],
# 'panelCapacityWatts': 400, 'panelHeightMeters': 1.879, 'panelWidthMeters': 1.045, 'panelLifetimeYears': 20,
# 'buildingStats': {'areaMeters2': 138.38115, 'sunshineQuantiles': [537, 728.5604, 799.23975, 833.99713, 900.88086,
# 959.65875, 1024.2743, 1086.1285, 1132.8774, 1162.1904, 1193.6012], 'groundAreaMeters2': 117.16}, 'solarPanels': [{
# 'center': {'latitude': 50.667371499999994, 'longitude': -4.7417235}, 'orientation': 'LANDSCAPE',
# 'yearlyEnergyDcKwh': 468.5037, 'segmentIndex': 0}, {'center': {'latitude': 50.6673614, 'longitude': -4.7417023},
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 467.61072, 'segmentIndex': 0}, {'center': {'latitude':
# 50.667365100000005, 'longitude': -4.7417311}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 465.55005,
# 'segmentIndex': 0}, {'center': {'latitude': 50.6673512, 'longitude': -4.741681000000001}, 'orientation':
# 'LANDSCAPE', 'yearlyEnergyDcKwh': 465.48712, 'segmentIndex': 0}, {'center': {'latitude': 50.667357599999995,
# 'longitude': -4.7416734}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 467.8553, 'segmentIndex': 0},
# {'center': {'latitude': 50.6673779, 'longitude': -4.741715999999999}, 'orientation': 'LANDSCAPE',
# 'yearlyEnergyDcKwh': 464.84396, 'segmentIndex': 0}, {'center': {'latitude': 50.6673678, 'longitude': -4.7416947},
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 464.79984, 'segmentIndex': 0}, {'center': {'latitude': 50.6673549,
# 'longitude': -4.7417098}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 461.58975, 'segmentIndex': 0},
# {'center': {'latitude': 50.6673816, 'longitude': -4.7417448}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh':
# 461.48065, 'segmentIndex': 0}, {'center': {'latitude': 50.6673881, 'longitude': -4.7417372}, 'orientation':
# 'LANDSCAPE', 'yearlyEnergyDcKwh': 458.3733, 'segmentIndex': 0}, {'center': {'latitude': 50.6673149, 'longitude':
# -4.7416768}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 457.68268, 'segmentIndex': 3}, {'center': {
# 'latitude': 50.6673204, 'longitude': -4.7416867}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 456.06827,
# 'segmentIndex': 3}, {'center': {'latitude': 50.667375199999995, 'longitude': -4.7417524}, 'orientation':
# 'LANDSCAPE', 'yearlyEnergyDcKwh': 453.20776, 'segmentIndex': 0}, {'center': {'latitude': 50.667364, 'longitude':
# -4.7416659}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 448.61087, 'segmentIndex': 0}, {'center': {
# 'latitude': 50.6673094, 'longitude': -4.741666899999999}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh':
# 440.66626, 'segmentIndex': 3}, {'center': {'latitude': 50.667403799999995, 'longitude': -4.741588900000001},
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 419.31348, 'segmentIndex': 2}, {'center': {'latitude':
# 50.66740850000001, 'longitude': -4.7416016999999995}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 418.74448,
# 'segmentIndex': 2}, {'center': {'latitude': 50.6673688, 'longitude': -4.7417599}, 'orientation': 'LANDSCAPE',
# 'yearlyEnergyDcKwh': 413.877, 'segmentIndex': 0}, {'center': {'latitude': 50.667348499999996, 'longitude':
# -4.7417174}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 411.76657, 'segmentIndex': 0}, {'center': {
# 'latitude': 50.6673587, 'longitude': -4.7417387}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 410.5925,
# 'segmentIndex': 0}, {'center': {'latitude': 50.6673992, 'longitude': -4.7415761}, 'orientation': 'LANDSCAPE',
# 'yearlyEnergyDcKwh': 404.15607, 'segmentIndex': 2}, {'center': {'latitude': 50.6674132, 'longitude': -4.7416145},
# 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh': 403.29822, 'segmentIndex': 2}, {'center': {'latitude': 50.6673324,
# 'longitude': -4.7417015}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 378.2754, 'segmentIndex': 4}, {'center':
# {'latitude': 50.667417799999996, 'longitude': -4.7416273}, 'orientation': 'LANDSCAPE', 'yearlyEnergyDcKwh':
# 373.53967, 'segmentIndex': 2}, {'center': {'latitude': 50.667324900000004, 'longitude': -4.7417104}, 'orientation':
# 'PORTRAIT', 'yearlyEnergyDcKwh': 365.37958, 'segmentIndex': 4}, {'center': {'latitude': 50.6674043, 'longitude':
# -4.741680800000001}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 341.4827, 'segmentIndex': 1}, {'center': {
# 'latitude': 50.667392299999996, 'longitude': -4.7416919}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh':
# 336.64502, 'segmentIndex': 1}, {'center': {'latitude': 50.667397, 'longitude': -4.741704599999999}, 'orientation':
# 'PORTRAIT', 'yearlyEnergyDcKwh': 339.7059, 'segmentIndex': 1}, {'center': {'latitude': 50.6674018, 'longitude':
# -4.7417174}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 336.25195, 'segmentIndex': 1}, {'center': {'latitude':
# 50.6673875, 'longitude': -4.7416791}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 331.08936, 'segmentIndex':
# 1}, {'center': {'latitude': 50.6674065, 'longitude': -4.7417301}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh':
# 325.05405, 'segmentIndex': 1}, {'center': {'latitude': 50.6673828, 'longitude': -4.7416664}, 'orientation':
# 'PORTRAIT', 'yearlyEnergyDcKwh': 321.63647, 'segmentIndex': 1}, {'center': {'latitude': 50.667378, 'longitude':
# -4.741653599999999}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 321.46332, 'segmentIndex': 1}, {'center': {
# 'latitude': 50.667373299999994, 'longitude': -4.7416409}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 339.3016,
# 'segmentIndex': 1}, {'center': {'latitude': 50.6673853, 'longitude': -4.7416298}, 'orientation': 'PORTRAIT',
# 'yearlyEnergyDcKwh': 327.26282, 'segmentIndex': 1}, {'center': {'latitude': 50.667399499999995, 'longitude':
# -4.741668}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 314.9878, 'segmentIndex': 1}, {'center': {'latitude':
# 50.6673948, 'longitude': -4.7416553}, 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 314.16364, 'segmentIndex':
# 1}, {'center': {'latitude': 50.667390000000005, 'longitude': -4.7416425}, 'orientation': 'PORTRAIT',
# 'yearlyEnergyDcKwh': 310.3404, 'segmentIndex': 1}, {'center': {'latitude': 50.6674186, 'longitude': -4.7417191},
# 'orientation': 'PORTRAIT', 'yearlyEnergyDcKwh': 278.3281, 'segmentIndex': 1}]}, 'boundingBox': {'sw': {'latitude':
# 50.6672904, 'longitude': -4.741778}, 'ne': {'latitude': 50.667431199999996, 'longitude': -4.7415536}},
# 'imageryQuality': 'MEDIUM', 'imageryProcessedDate': {'year': 2024, 'month': 4, 'day': 18}}
self.insights_data = self.get_building_insights(longitude, latitude, required_quality)
# Extract key data from the insights response
self.roof_segments = self.insights_data["solarPotential"].get('roofSegmentStats', [])
self.floor_area = self.insights_data["solarPotential"]["wholeRoofStats"]['groundAreaMeters2']
self.roof_area = self.insights_data["solarPotential"]["wholeRoofStats"]['areaMeters2']
self.panel_area = (
self.insights_data["solarPotential"]["panelHeightMeters"] *
self.insights_data["solarPotential"]["panelWidthMeters"]
)
self.panel_wattage = self.insights_data["solarPotential"]["panelCapacityWatts"]
if self.panel_wattage != 400:
# In the API documentation, it claims that the default output is 250W, however we've only seen 400W, so if
# we get anything other than 400W, we'll need to adjust the calculations in the output. For this, we should
# refer to https://developers.google.com/maps/documentation/solar/calculate-costs-non-us
# Where the documentation explains how to adjust the yearlyEnergyDcKwh figures.
# It should be straightforward, but I'd rather see an actual instance of this happening
raise NotImplementedError("Panel wattage is not 400W - implement me")
# Automatically exclude north-facing segments
self.exclude_north_facing_segments()
self.roof_segment_indexes = [segment['segmentIndex'] for segment in self.roof_segments]
# We now start finding the solar panel configurations
self.optimise_solar_configuration()
@staticmethod
def lifetime_production_ac_kwh(
row,
efficiency_depreciation_factor,
installation_life_span
):
"""
Mimics the function described in the Google Solar API documentation, presenting the lifetime production
AC KWH as a geometri sum
"""
return (
row["initial_ac_kwh_per_year"] *
(1 - pow(
efficiency_depreciation_factor,
installation_life_span)) /
(1 - efficiency_depreciation_factor))
@staticmethod
def annualUtilityBillEstimate(
yearlyKWhEnergyConsumption,
initialAcKwhPerYear,
efficiencyDepreciationFactor,
year,
costIncreaseFactor,
discountRate):
"""
Implements the bill costing model for esimating annual bill
:param yearlyKWhEnergyConsumption:
:param initialAcKwhPerYear:
:param efficiencyDepreciationFactor:
:param year:
:param costIncreaseFactor:
:param discountRate:
:return:
"""
return (
billCostModel(
yearlyKWhEnergyConsumption -
annualProduction(
initialAcKwhPerYear,
efficiencyDepreciationFactor,
year)) *
pow(costIncreaseFactor, year) /
pow(discountRate, year))
def lifetimeUtilityBill(
yearlyKWhEnergyConsumption,
initialAcKwhPerYear,
efficiencyDepreciationFactor,
installationLifeSpan,
costIncreaseFactor,
discountRate):
bill = [0] * installationLifeSpan
for year in range(installationLifeSpan):
bill[year] = annualUtilityBillEstimate(
yearlyKWhEnergyConsumption,
initialAcKwhPerYear,
efficiencyDepreciationFactor,
year,
costIncreaseFactor,
discountRate)
return bill
def estimate_solar_costs(self, panel_performance):
"""
This method implements the recommended costing approach, to estimate the ROI of a solar panel
configuration, as described in the Google Solar API documentation
:param panel_performance: dataframe containing the solar panel array configuration and energy generation data
:return:
"""
# we now estiamte the financial benefits of solar panels for the household, using the framework described
# by the Google Solar API
# 1) Convert Solar Energy AD production from the DC production
panel_performance["initial_ac_kwh_per_year"] = panel_performance["yearly_dc_energy"] * self.dc_to_ac_rate
# Remove anything where the total ac energy is less than half of the array wattage
panel_performance = panel_performance[
(panel_performance["initial_ac_kwh_per_year"] / panel_performance["array_warrage"]) >= 0.5
]
# 2) Calculate the liftime solar energy production
panel_performance['lifetime_ac_kwh'] = panel_performance.apply(
self.lifetime_production_ac_kwh,
axis=1,
efficiency_depreciation_factor=self.efficiency_depreciation_factor,
installation_life_span=self.installation_life_span
)
# TODO: Complete the rest of the solar model
def optimise_solar_configuration(self):
"""
Optimise the solar panel configuration for the building.
:return:
"""
# Remove any north facing roof segments
panel_performance = []
for config in self.insights_data["solarPotential"]["solarPanelConfigs"]:
roof_segment_summaries = config["roofSegmentSummaries"]
# Filter on just the segments in self.roof_segment_indexes
roof_segment_summaries = [
segment for segment in roof_segment_summaries if segment["segmentIndex"] in self.roof_segment_indexes
]
roi_summary = []
for segment in roof_segment_summaries:
wattage = segment["panelsCount"] * self.insights_data["solarPotential"]["panelCapacityWatts"]
generated_dc_energy = segment["yearlyEnergyDcKwh"]
ratio = generated_dc_energy / wattage
cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (generated_dc_energy / 1000)
roi_summary.append(
{
"segmentIndex": segment["segmentIndex"],
"wattage": wattage,
"generated_dc_energy": generated_dc_energy,
"ratio": ratio,
"n_panels": segment["panelsCount"],
"cost": cost,
"panneled_roof_area": self.panel_area * int(segment["panelsCount"])
}
)
roi_summary = pd.DataFrame(roi_summary)
weighted_ratio = np.average(
roi_summary["ratio"].values, weights=roi_summary["generated_dc_energy"].values
)
total_cost = roi_summary["cost"].sum()
yearly_dc_energy = roi_summary["generated_dc_energy"].sum()
panel_performance.append(
{
"n_panels": roi_summary["n_panels"].sum(),
"yearly_dc_energy": yearly_dc_energy,
"total_cost": total_cost,
"weighted_ratio": weighted_ratio,
"panneled_roof_area": roi_summary["panneled_roof_area"].sum(),
"array_warrage": roi_summary["n_panels"].sum() * self.panel_wattage
}
)
panel_performance = pd.DataFrame(panel_performance)
# We can have duplicate configurations
panel_performance = panel_performance.drop_duplicates()
# Ensure more than 4 panels
panel_performance = panel_performance[panel_performance["n_panels"] >= 4]
self.estimate_solar_costs()
# This first bracket is the value of the energy bill savings
panel_performance["bill_savings"] = (
self.SOLAR_CONSUMPTION_PROPORTION *
panel_performance["total_energy"] *
AnnualBillSavings.ELECTRICITY_PRICE_CAP
)
# This is the amount of energy exported
panel_performance["export_value"] = (
(1 - self.SOLAR_CONSUMPTION_PROPORTION) *
panel_performance["total_energy"] *
AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
)
panel_performance["energy_value"] = panel_performance["bill_savings"] + panel_performance["export_value"]
panel_performance["payback_years"] = panel_performance["total_cost"] / panel_performance["energy_value"]
panel_performance = panel_performance.sort_values("weighted_ratio", ascending=False)
# TODO: Finish this!!
panel_performance["roof_area_percentage"] = panel_performance["panneled_roof_area"] / self.roof_area
self.panel_performance = panel_performance
def exclude_north_facing_segments(self):
"""
Filter out any north-facing roof segments from the roof_segments attribute.
North-facing segments are defined as those with an azimuth between -30 and 30 degrees.
"""
filtered_segments = []
for segment_index, segment in enumerate(self.roof_segments):
segment["segmentIndex"] = segment_index
# Check if the segment is north-facing
if self.NORTH_FACING_AZIMUTH_RANGE[0] <= segment['azimuthDegrees'] <= self.NORTH_FACING_AZIMUTH_RANGE[1]:
continue
filtered_segments.append(segment)
self.roof_segments = filtered_segments

1
backend/app/config.py
View file

@ -14,6 +14,7 @@ class Settings(BaseSettings):
PLAN_TRIGGER_BUCKET: str
EPC_AUTH_TOKEN: str
ORDNANCE_SURVEY_API_KEY: str
GOOGLE_SOLAR_API_KEY: str
DB_HOST: str
DB_PASSWORD: str
DB_USERNAME: str

351
backend/app/plan/router.py
View file

@ -23,12 +23,13 @@ from backend.app.db.functions.recommendations_functions import (
)
from backend.app.db.models.portfolio import rating_lookup
from backend.app.dependencies import validate_token
from backend.app.plan.schemas import PlanTriggerRequest
from backend.app.plan.schemas import PlanTriggerRequest, MdsRequest
from backend.app.plan.utils import get_cleaned
from backend.app.utils import epc_to_sap_lower_bound, sap_to_epc
from backend.ml_models.api import ModelApi
from backend.Property import Property
from backend.apis.GoogleSolarApi import GoogleSolarApi
from etl.solar.SolarPhotoSupply import SolarPhotoSupply
from recommendations.optimiser.CostOptimiser import CostOptimiser
@ -347,10 +348,14 @@ async def trigger_plan(body: PlanTriggerRequest):
bucket_name=get_settings().DATA_BUCKET, file_key="spatial/filename_meta.parquet"
)
photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket=get_settings().DATA_BUCKET)
solar_api_client = GoogleSolarApi(api_key=get_settings().GOOGLE_SOLAR_API_KEY)
logger.info("Getting spatial data")
for p in input_properties:
p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
p.get_spatial_data(uprn_filenames)
# Call Google Solar API
solar_performance = solar_api_client.get(longitude=p.spatial["longitude"], latitude=p.spatial["latitude"])
logger.info("Getting components and epc recommendations")
recommendations = {}
@ -358,9 +363,6 @@ async def trigger_plan(body: PlanTriggerRequest):
representative_recommendations = {}
for p in tqdm(input_properties):
# Property recommendations
p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
recommender = Recommendations(property_instance=p, materials=materials, exclusions=body.exclusions)
property_recommendations, property_representative_recommendations = recommender.recommend()
@ -422,9 +424,7 @@ async def trigger_plan(body: PlanTriggerRequest):
(
recommendations_with_impact,
current_adjusted_energy,
expected_adjusted_energy,
current_energy_bill,
expected_energy_bill
) = (
Recommendations.calculate_recommendation_impact(
@ -436,9 +436,7 @@ async def trigger_plan(body: PlanTriggerRequest):
# Store the resulting adjusted energy in the property instance
property_instance.set_adjusted_energy(
current_adjusted_energy=current_adjusted_energy,
expected_adjusted_energy=expected_adjusted_energy,
current_energy_bill=current_energy_bill,
expected_energy_bill=expected_energy_bill
)
@ -622,7 +620,7 @@ async def trigger_plan(body: PlanTriggerRequest):
@router.post("/mds")
async def build_mds(body: PlanTriggerRequest):
async def build_mds(body: MdsRequest):
# TODO: This is a placeholder location for the MDS endpoint, which this is being assembled
logger.info("Connecting to db")
@ -633,6 +631,8 @@ async def build_mds(body: PlanTriggerRequest):
session.begin()
logger.info("Getting the inputs")
plan_input = read_csv_from_s3(bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.trigger_file_path)
measure_set = body.measures
optimise_measures = measure_set is not None
cleaning_data = read_dataframe_from_s3_parquet(
bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
@ -659,10 +659,14 @@ async def build_mds(body: PlanTriggerRequest):
epc_searcher.find_property(skip_os=True)
if config["address"] == "35b High Street":
print("Performing temporary patch")
print("Performing temporary patch on 35b High Street")
epc_searcher.newest_epc["uprn"] = 10002911892
epc_searcher.full_sap_epc["uprn"] = 10002911892
if config["address"] == "Cobnut Barn":
print("Performing temporary patch on Cobnut Barn")
epc_searcher.newest_epc["uprn"] = 10013924689
# Create a record in db
# TODO: If we productionise the creation of this mds report, we will need to store this in the db
# property_id, is_new = create_property(
@ -706,7 +710,10 @@ async def build_mds(body: PlanTriggerRequest):
# (x["address"] == config["address"]) and (x["postcode"] == config["postcode"])
# ), {})
measures = config["measures"] if "measures" in config else None
if measure_set is None:
measures = config["measures"] if "measures" in config else None
else:
measures = measure_set
input_properties.append(
Property(
@ -737,24 +744,49 @@ async def build_mds(body: PlanTriggerRequest):
logger.info("Getting components and epc recommendations")
recommendations_scoring_data = []
representative_recommendations = {}
recommendations = {}
for p in tqdm(input_properties):
p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
mds = Mds(property_instance=p, materials=materials)
property_representative_recommendations, errors = mds.build()
mds = Mds(property_instance=p, materials=materials, optimise_measures=optimise_measures)
mds_recommendations, property_representative_recommendations, errors = mds.build()
if errors:
logger.info("Errors occurred during MDS build")
if isinstance(errors, list):
if errors:
raise Exception("Errors occurred during MDS build")
else:
if any([len(x) for x in errors.values()]):
raise Exception("Errors occurred during MDS build")
recommendations[p.id] = mds_recommendations
representative_recommendations[p.id] = property_representative_recommendations
# Build the scoring data
p.create_base_difference_epc_record(cleaned_lookup=cleaned)
recommendations_scoring_data.append(
p.simulate_all_representative_recommendations(property_representative_recommendations)
)
if optimise_measures:
for _id, mds_recs in mds_recommendations.items():
representative_ids = [r["recommendation_id"] for r in property_representative_recommendations[_id]]
simulation_mds_recs = []
for recs in mds_recs:
simulation_mds_recs.append(
[r for r in recs if r["recommendation_id"] in representative_ids]
)
p.adjust_difference_record_with_recommendations(
simulation_mds_recs, property_representative_recommendations[_id]
)
data = p.recommendations_scoring_data.copy()
for d in data:
d["id"] = d["id"] + "*" + _id
recommendations_scoring_data.extend(data)
else:
recommendations_scoring_data.append(
p.simulate_all_representative_recommendations(property_representative_recommendations)
)
logger.info("Preparing data for scoring in sap change api")
recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
@ -787,13 +819,198 @@ async def build_mds(body: PlanTriggerRequest):
for key, scored in predictions_dict.items():
all_predictions[key] = pd.concat([all_predictions[key], scored])
# We now produce a table of results for the mds report
# TODO: 1) walls_insulation_thickness_ending is not being set in the recommendations_scoring_data,
# insulation_thickness_ending is being set instead
# 2)
# TODO: TEMP
for p in plan_input:
if p["uprn"]:
p["uprn"] = str(int(float(p["uprn"])))
import re
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
if optimise_measures:
results = []
for p in input_properties:
sap_before = int(p.data["current-energy-efficiency"])
epc_before = p.data["current-energy-rating"]
heat_demand_before = p.data["energy-consumption-current"]
carbon_before = p.data["co2-emissions-current"]
current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
epc_energy_consumption=heat_demand_before * p.floor_area,
current_epc_rating=epc_before,
)
current_energy_bill = AnnualBillSavings.calculate_annual_bill(current_adjusted_energy)
package_comparison = []
for _id in recommendations[p.id].keys():
sap_prediction = all_predictions["sap_change_predictions"][
(all_predictions["sap_change_predictions"]["property_id"] == str(p.id)) &
(all_predictions["sap_change_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
].copy().reset_index(drop=True)
sap_prediction["row_id"] = sap_prediction.index
heat_demand_prediction = all_predictions["heat_demand_predictions"][
(all_predictions["heat_demand_predictions"]["property_id"] == str(p.id)) &
(all_predictions["heat_demand_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
].copy().reset_index(drop=True)
heat_demand_prediction["row_id"] = heat_demand_prediction.index
carbon_prediction = all_predictions["carbon_change_predictions"][
(all_predictions["carbon_change_predictions"]["property_id"] == str(p.id)) &
(all_predictions["carbon_change_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
].copy().reset_index(drop=True)
carbon_prediction["row_id"] = carbon_prediction.index
epc_target = body.goal_value
if epc_before == epc_target:
continue
sap_target = epc_to_sap_lower_bound(epc_target)
# Define the measures
sap_threshold_barrier = sap_prediction[sap_prediction["predictions"] >= sap_target]
meets_threshold = True
if sap_threshold_barrier.empty:
sap_threshold_barrier = sap_prediction.tail(1)
meets_threshold = False
sap_threshold_barrier = sap_threshold_barrier.head(1)
sap_prediction = sap_prediction[
sap_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
]
heat_demand_prediction = heat_demand_prediction[
heat_demand_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
]
carbon_prediction = carbon_prediction[
carbon_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
]
reverse_map = {v: k for k, v in Mds.format_map.items()}
selected_measures = [
reverse_map[x.split("-")[0]] for x in sap_prediction["recommendation_id"].values
]
selected_measure_ids = [x.split("*")[0] for x in sap_prediction["recommendation_id"].values]
costs = [
r["total"] for r in representative_recommendations[p.id][_id] if
r["recommendation_id"] in selected_measure_ids
]
costs = sum(costs)
sap_after = sap_prediction["predictions"].values[-1]
epc_after = sap_to_epc(sap_after)
heat_demand_after = heat_demand_prediction["predictions"].values[-1]
carbon_after = carbon_prediction["predictions"].values[-1]
expected_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
epc_energy_consumption=heat_demand_after * p.floor_area,
current_epc_rating=epc_before,
)
expected_energy_bill = AnnualBillSavings.calculate_annual_bill(expected_adjusted_energy)
bill_savings = current_energy_bill - expected_energy_bill
energy_savings = current_adjusted_energy - expected_adjusted_energy
package_comparison.append(
{
"id": _id,
"cost": costs,
"measures": selected_measures,
"sap_before": sap_before,
"sap_after": sap_after,
"epc_before": epc_before,
"epc_after": epc_after,
"heat_demand_before": heat_demand_before,
"heat_demand_after": heat_demand_after,
"carbon_before": carbon_before,
"carbon_after": carbon_after,
"bill_savings": bill_savings,
"energy_savings": energy_savings,
"current_energy_bill": current_energy_bill,
"meets_threshold": meets_threshold
}
)
package_comparison = pd.DataFrame(package_comparison)
# Find the smallest cost package
if not package_comparison.empty:
# We check if any of the packages meet the threshold
# If none of them do, take the one that gets closest to the target
if package_comparison["meets_threshold"].any():
package_comparison = package_comparison[package_comparison["meets_threshold"]]
package_comparison = package_comparison.sort_values("cost")
else:
package_comparison = package_comparison.sort_values("sap_after", ascending=False)
package_comparison = package_comparison.head(1).to_dict("records")[0]
else:
package_comparison = {
"measures": [],
"sap_before": sap_before,
"sap_after": sap_before,
"epc_before": epc_before,
"epc_after": epc_before,
"heat_demand_before": heat_demand_before,
"heat_demand_after": heat_demand_before,
"carbon_before": carbon_before,
"carbon_after": carbon_before,
"bill_savings": 0,
"energy_savings": 0,
"current_energy_bill": current_energy_bill,
"meets_threshold": False
}
config = [c for c in plan_input if c["uprn"] == str(p.uprn)]
if not config:
config = {"address": None, "postcode": None}
else:
config = config[0]
results.append({
"config_address": config["address"],
"config_postcode": config["postcode"],
"uprn": p.uprn,
"address": p.address,
"postcode": p.postcode,
"measures": package_comparison["measures"],
"year_of_epc": p.data['lodgement-date'],
"sap_before": package_comparison["sap_before"],
"sap_after": package_comparison["sap_after"],
"epc_before": package_comparison["epc_before"],
"epc_after": package_comparison["epc_after"],
"heat_demand_before": package_comparison["heat_demand_before"],
"heat_demand_after": package_comparison["heat_demand_after"],
"carbon_before": package_comparison["carbon_before"],
"carbon_after": package_comparison["carbon_after"],
"bill_savings": round(package_comparison["bill_savings"], 2),
"energy_savings": round(package_comparison["energy_savings"], 2),
"current_energy_bill": round(package_comparison["current_energy_bill"], 2),
"EWI": "EWI" if "external_wall_insulation" in package_comparison["measures"] else None,
"CWI": "CWI" if "cavity_wall_insulation" in package_comparison["measures"] else None,
"LI": "LI" if "loft_insulation" in package_comparison["measures"] else None,
"ASHP Htg": "ASHP Htg" if "air_source_heat_pump" in package_comparison["measures"] else None,
"Elec Storage": (
"Elec Storage Htrs (Out of scope -Prov sum only)" if "high_heat_retention_storage_heaters" in
package_comparison["measures"] else None
),
"Solar PV": "Solar PV" if "solar_pv" in package_comparison["measures"] else None,
})
results = pd.DataFrame(results)
# For the different measures, we check the impact with a few debugging functions
walls_check, hhr_check = check_mds(results, input_properties, recommendations, optimise_measures)
results.to_excel("optimised mds_results 5th June.xlsx")
results = []
for p in input_properties:
measures = p.measures
@ -842,11 +1059,14 @@ async def build_mds(body: PlanTriggerRequest):
)
# TODO: We should determine if the home is gas & electricity or just electricity
# Determine if the heating and hotwater was previously electric only or both
current_energy_bill = AnnualBillSavings.calculate_annual_bill(
current_adjusted_energy,
kwh=current_adjusted_energy,
)
expected_energy_bill = AnnualBillSavings.calculate_annual_bill(
expected_adjusted_energy,
kwh=expected_adjusted_energy,
)
bill_savings = current_energy_bill - expected_energy_bill
@ -861,6 +1081,7 @@ async def build_mds(body: PlanTriggerRequest):
to_append = {
"config_address": config["address"],
"config_postcode": config["postcode"],
"uprn": p.uprn,
"address": p.address,
"postcode": p.postcode,
"measures": measures,
@ -874,14 +1095,19 @@ async def build_mds(body: PlanTriggerRequest):
"heat_demand_after": heat_demand_after,
"carbon_before": carbon_before,
"carbon_after": carbon_after,
"bill_savings": bill_savings,
"energy_savings": energy_savings,
"bill_savings": round(bill_savings, 2),
"energy_savings": round(energy_savings, 2),
"current_energy_bill": round(current_energy_bill, 2),
"fuel_type": p.main_fuel["fuel_type"],
}
results.append(to_append)
results = pd.DataFrame(results)
results["sap_uplift"] = results["sap_after"] - results["sap_before"]
# results.to_excel("mds_results 5th June.xlsx")
walls_check, hhr_check = check_mds(results, input_properties, recommendations, optimise_measures)
except IntegrityError:
logger.error("Database integrity error occurred", exc_info=True)
@ -901,3 +1127,80 @@ async def build_mds(body: PlanTriggerRequest):
return Response(status_code=500, content="An unexpected error occurred.")
finally:
session.close()
def check_mds(results, input_properties, recommendations, optimise_measures):
import ast
walls_check = []
hhr_check = []
for p in input_properties:
res = results[results["uprn"] == p.uprn]
wall = p.walls
heating = p.main_heating
heating_controls = p.main_heating_controls
if optimise_measures:
measures = res["measures"].values[0]
else:
measures = [list(z.keys())[0] for z in res["measures"].values[0]]
wall_recommendation = [
x for x in measures if
x in ["internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation"]
]
hhr_recommendation = [
x for x in measures if
x in ["high_heat_retention_storage_heaters"]
]
if optimise_measures:
possible_measures = [ast.literal_eval(x) for x in list(recommendations[p.id].keys())]
# Unlist them
possible_measures = [x for sublist in possible_measures for x in sublist]
possible_measures = list(set(possible_measures))
else:
possible_measures = p.measures
if wall_recommendation:
if len(wall_recommendation) > 1:
raise Exception("something went wrong")
wall_recommendation = wall_recommendation[0]
else:
wall_recommendation = None
hhr_recommendation = hhr_recommendation[0] if hhr_recommendation else None
walls_check.append(
{
"uprn": p.uprn,
"address": p.address,
"postcode": p.postcode,
"property_type": p.data['property-type'],
"conservation_status": p.spatial["conservation_status"],
"is_listed_building": p.spatial["is_listed_building"],
"is_heritage_building": p.spatial["is_heritage_building"],
"wall": wall["clean_description"],
"recommendation": wall_recommendation,
"possible_measures": possible_measures,
"selected_measures": res["measures"].values[0],
}
)
hhr_check.append(
{
"uprn": p.uprn,
"address": p.address,
"postcode": p.postcode,
"heating": heating["clean_description"],
"heating_controls": heating_controls["clean_description"],
"recommendation": hhr_recommendation,
"possible_measures": possible_measures,
"selected_measures": res["measures"].values[0],
}
)
walls_check = pd.DataFrame(walls_check)
hhr_check = pd.DataFrame(hhr_check)
return walls_check, hhr_check

6
backend/app/plan/schemas.py
View file

@ -52,3 +52,9 @@ class PlanTriggerRequest(BaseModel):
if v not in cls._allowed_housing_types:
raise ValueError(f"{v} is not a valid housing type")
return v
class MdsRequest(PlanTriggerRequest):
# When creating the mds report, we allow an optional list of measures to select from. If this is passed, it will
# cause the service to select the optimal package from the list of measures
measures: Optional[conlist(str, min_items=1)] = None

64
backend/ml_models/AnnualBillSavings.py
View file

@ -1,3 +1,6 @@
import numpy as np
class AnnualBillSavings:
"""
This is a simple class which will estimate the annual bill savings, based on the kwh savings.
@ -14,6 +17,8 @@ class AnnualBillSavings:
# https://www.ofgem.gov.uk/publications/new-energy-price-cap-level-april-june-2024-starts-today
ELECTRICITY_PRICE_CAP = 0.245
GAS_PRICE_CAP = 0.0604
# This is the most recent export payment figure, at 12p per kwh
ELECTRICITY_EXPORT_PAYMENT = 0.12
# This is a weighted mean of the price caps, using the consumption figures above as weights
PRICE_FACTOR = 0.09549999999999999
@ -58,8 +63,58 @@ class AnnualBillSavings:
return cls.ELECTRICITY_PRICE_CAP * kwh + (cls.DAILY_STANDARD_CHARGE_ELECTRICITY * 365)
@staticmethod
def calculate_occupants(total_floor_area):
"""
From Table 1b of the SAP 2012 documentation https://bregroup.com/documents/d/bre-group/sap-2012_9-92
Provides a methodology to estimate occupancy, based on floor area. This is used to calculate the amount of
electricity used be appliances and during cooking.
:param total_floor_area:
:return:
"""
if total_floor_area <= 13.9:
return 1
return 1 + (1.76 * (1 - np.exp(-0.000349 * (total_floor_area - 13.9) * (total_floor_area - 13.9))) + 0.0013 * (
total_floor_area - 13.9))
@staticmethod
def estimate_electrical_appliances(occupants, total_floor_area):
"""
From secion L2 of SAP2012 Electrical appliances
https://bregroup.com/documents/d/bre-group/sap-2012_9-92
Used to estimate the amount of energy used by electrical appliances
:param occupants:
:param total_floor_area:
:return:
"""
e_a = 207.8 * np.power(total_floor_area * occupants, 0.4717)
days_in_month = {
1: 31,
2: 28,
3: 31,
4: 30,
5: 31,
6: 30,
7: 31,
8: 31,
9: 30,
10: 31,
11: 30,
12: 31
}
eam = 0
for m in range(1, 13):
nm = days_in_month[m]
eam += e_a * (1 + 0.157 * np.cos(2 * np.pi * (m - 1.78) / 12)) * nm / 365
return eam
@classmethod
def adjust_energy_to_metered(cls, epc_energy_consumption, current_epc_rating):
def adjust_energy_to_metered(cls, epc_energy_consumption, current_epc_rating, total_floor_area):
"""
The over-prediction of energy use by EPCs in Great Britain: A comparison
of EPC-modelled and metered primary energy use intensity
@ -70,6 +125,13 @@ class AnnualBillSavings:
:return:
"""
# The EPC energy consumption does not factor in cooking and applicance use, so this is estimated using the
# methodology outlined in SAP, and is discussed in the UCL paper in section 3.1.1
estimated_occupants = cls.calculate_occupants(total_floor_area=total_floor_area)
appliances_energy_use = cls.estimate_electrical_appliances(estimated_occupants, total_floor_area)
epc_energy_consumption += appliances_energy_use
gradients = {
"A": -0.1,
"B": -0.1,

3
backend/ml_models/Valuation.py
View file

@ -90,6 +90,9 @@ class PropertyValuation:
41222760: 46_000, # Based on Zoopla
41222761: 270_000, # Based on Zoopla
41212534: 38_000, # Based on Zoopla
# Northern Group Pilot - search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
10070868263: 194_000, # Based on Zoopla
10070868244: 195_000, # Based on Zoopla
}
# We base our valuation uplifts on a number of sources

27
etl/customers/eon/deck_examples.py Normal file
View file

@ -0,0 +1,27 @@
"""
This script contains bits of codes for examples to be included in the Deck
"""
from backend.SearchEpc import SearchEpc
from dotenv import load_dotenv
import os
load_dotenv(dotenv_path="backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
searcher = SearchEpc(
address1="108 Blacklands",
postcode="ME19 6DP",
auth_token=EPC_AUTH_TOKEN,
os_api_key="",
property_type=None,
fast=False,
)
res = searcher.estimate_epc(
property_type="Bungalow",
built_form="Detached",
lmks_to_drop=["849273656952012102323315196229804"],
exclude_old=True
)

35
etl/customers/eon/pilot_asset_list.py
View file

@ -64,7 +64,7 @@ def extract_mds_measures(config):
measures.append({"district_heating_networks": "District heating networks"})
if not pd.isnull(config["Elec Storage Htrs (Out of scope -Prov sum only)"]):
measures.append({"electric_storage_heaters": "Elec Storage Htrs (Out of scope -Prov sum only)"})
measures.append({"high_heat_retention_storage_heaters": "Elec Storage Htrs (Out of scope -Prov sum only)"})
if not pd.isnull(config["Low Energy Bulbs"]):
measures.append({"low_energy_lighting": "Low Energy Bulbs"})
@ -229,7 +229,8 @@ def app():
"35a High Street",
"35b High Street",
"Flat Over 20 Holborough Road",
"Flat above 7 Malling Road"
"Flat above 7 Malling Road",
"Cobnut Barn",
]:
print(config["Address"])
uprn = None
@ -269,3 +270,33 @@ def app():
"budget": None,
}
print(body)
# Optimised version where we specify the measures
measures = [
"external_wall_insulation",
"cavity_wall_insulation",
"loft_insulation",
"air_source_heat_pump",
"high_heat_retention_storage_heaters",
"solar_pv"
]
body = {
"portfolio_id": str(PORTFOLIO_ID),
"housing_type": "Social",
"goal": "Increase EPC",
"goal_value": "C",
"trigger_file_path": filename,
"already_installed_file_path": "",
"patches_file_path": "",
"non_invasive_recommendations_file_path": "",
"measures": measures,
"budget": None,
}
output = []
for r in self.results:
output.append(r["DPA"])
output = pd.DataFrame(output)

113
etl/customers/goldman/property_ownership.py
View file

@ -3,6 +3,7 @@ import pandas as pd
from tqdm import tqdm
import Levenshtein
from backend.SearchEpc import SearchEpc
from utils.s3 import read_dataframe_from_s3_parquet
# Average value of a property in the midlands in 2024 was £238,000. Since these are EPC F & G properties, we assume
# £207,000 since they trade at a discount. This is based on the rightmove study where moving from an EPC F/G -> C has a
@ -248,6 +249,13 @@ def app():
"""
This script is for scoping property ownership for EPC F & G rated properties in Birmingam, for Goldman Sachs
"""
# TODO: This property:
# https://epc.opendatacommunities.org/domestic/search?address=&postcode=&local-authority=&constituency
# =&uprn=100031179243&from-month=1&from-year=2008&to-month=12&to-year=2024
# is actually listed in two local authorities causing us to think it's an EPC F & G property, but it's
# it's actually EPC E. Need to handle this, probably by reading in all of the EPC data, concatenating together
# and performing a singular filter for most recent EPC by UPRN
# paths = [
# "local_data/all-domestic-certificates/domestic-E08000025-Birmingham/certificates.csv",
# "local_data/all-domestic-certificates/domestic-E08000031-Wolverhampton/certificates.csv",
@ -477,6 +485,35 @@ def app():
portfolio_epc_data_50m.to_excel("portfolio_epc_data_50m 28th May.xlsx", index=False)
portfolio_epc_data_20m.to_excel("portfolio_epc_data_20m 28th May.xlsx", index=False)
# We check if any of these properties are in a conservation area
valuations = pd.read_excel("property value.xlsx")
uprn_filenames = read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev", file_key="spatial/filename_meta.parquet"
)
geospatial_data = []
for _, row in tqdm(valuations.iterrows(), total=len(valuations)):
filtered_df = uprn_filenames[
(uprn_filenames["lower"] <= row["UPRN"])
& (uprn_filenames["upper"] >= row["UPRN"])
]
if filtered_df.empty:
raise Exception("No match found")
filename = filtered_df.iloc[0]["filenames"]
spatial_data = read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev", file_key=f"spatial/{filename}"
)
spatial = spatial_data[
spatial_data["UPRN"] == row["UPRN"]
][["UPRN", "conservation_status", "is_listed_building", "is_heritage_building"]]
geospatial_data.append(spatial.to_dict("records")[0])
geospatial_data = pd.DataFrame(geospatial_data)
geospatial_data.to_excel("geospatial_data.xlsx", index=False)
def company_aggregation():
company_ownership = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/CCOD_FULL_2024_04.csv")
@ -490,3 +527,79 @@ def company_aggregation():
aggregation = aggregation.sort_values("Number of Properties", ascending=False)
aggregation.to_excel("Company ownership aggregation.xlsx")
def prepare_anonymised_data():
investment_50m_properties = pd.read_excel("investment_50m_properties 28th May.xlsx", header=0)
investment_epc_data = pd.read_excel("portfolio_epc_data_50m 28th May.xlsx", header=0)
valuations = pd.read_excel("property value.xlsx", header=0)
# Merge these datasets
df = investment_50m_properties.merge(
investment_epc_data[
["UPRN", "PROPERTY_TYPE", "BUILT_FORM", "TOTAL_FLOOR_AREA", "LODGEMENT_DATE", "POSTCODE"]
].rename(
columns={
"PROPERTY_TYPE": "Property Type",
"BUILT_FORM": "Property Archetype",
"TOTAL_FLOOR_AREA": "Total Floor Area",
"LODGEMENT_DATE": "Date EPC Lodged",
"POSTCODE": "Postcode on EPC"
}
),
how="inner",
on="UPRN"
).merge(
valuations.drop(columns=["ADDRESS", "POSTCODE"]).rename(
columns={
"Zoopla Valuation": "Expected Valuation",
"Zoopla Lower Bound": "Valuation - Lower Bound",
"Zoopla Upper Bound": "Valuation - Upper Bound",
}
),
how="inner",
on="UPRN"
).rename(
columns={
"CURRENT_ENERGY_RATING": "Current EPC",
"CURRENT_ENERGY_EFFICIENCY": "Current SAP Score",
"epc_address": "Address on EPC"
}
).drop(
columns=["Title Number", "match_type", "UPRN"]
)
redacted_owner_names = df[["Company Registration No. (1)"]].drop_duplicates()
redacted_owner_names["Owner"] = ["Owner" + str(i) for i in range(1, len(redacted_owner_names) + 1)]
df = df.merge(
redacted_owner_names, how="left", on="Company Registration No. (1)"
)
df = df.drop(columns=["Company Registration No. (1)", "Proprietor Name (1)", "Property Address"])
df = df.sort_values(["Owner", "Date EPC Lodged"], ascending=False)
redacted_index = []
for _, owner_properties in df.groupby("Owner"):
top_50_percent = round(owner_properties.shape[0] / 2 + 0.00001)
indexes = owner_properties.tail(
owner_properties.shape[0] - top_50_percent
).index
redacted_index.extend(indexes.tolist())
import numpy as np
# Redact addresses and postcodes
df["Address on EPC"] = np.where(
df.index.isin(redacted_index),
"Redacted",
df["Address on EPC"]
)
df["Postcode on EPC"] = np.where(
df.index.isin(redacted_index),
"Redacted",
df["Postcode on EPC"]
)
df.to_excel("Property List - 50% redacted.xlsx", index=False)

148
etl/customers/lhp/30_may_2024_data_pull.py Normal file
View file

@ -0,0 +1,148 @@
import os
import pandas as pd
from tqdm import tqdm
from dotenv import load_dotenv
from utils.s3 import read_excel_from_s3
from backend.SearchEpc import SearchEpc
from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes
from recommendations.recommendation_utils import (
estimate_perimeter,
estimate_external_wall_area,
estimate_number_of_floors
)
load_dotenv(dotenv_path="backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
def app():
"""
This app is EPC pulling data for some properties owned by LHP
:return:
"""
# asset_list = read_excel_from_s3(
# bucket_name="retrofit-datalake-dev",
# file_key="customers/guiness/TGP CW Properties PV.xlsx",
# header_row=0
# )
asset_list = pd.read_excel("/Users/khalimconn-kowlessar/Downloads/Echo4 3.4.24.xlsx", header=0)
epc_data = []
for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
full_address = home["ADDRESS"]
address_split = full_address.split(",")
address1 = address_split[0].strip()
postcode = address_split[-1].strip()
searcher = SearchEpc(
address1=address1,
postcode=postcode,
auth_token=EPC_AUTH_TOKEN,
os_api_key="",
property_type=None,
fast=True,
full_address=full_address
)
# Force the skipping of estimating the EPC
searcher.ordnance_survey_client.property_type = None
searcher.ordnance_survey_client.built_form = None
searcher.find_property(skip_os=True)
if searcher.newest_epc is None:
continue
epc = {
"asset_list_address": full_address,
**searcher.newest_epc.copy()
}
epc_data.append(epc)
epc_df = pd.DataFrame(epc_data)
# Retrieve just the data we need
epc_df = epc_df[
[
"asset_list_address",
"uprn",
"property-type",
"built-form",
"inspection-date",
"current-energy-rating",
"current-energy-efficiency",
"roof-description",
"walls-description",
"transaction-type",
# New fields needed
"secondheat-description",
"total-floor-area",
"construction-age-band",
"floor-height",
"number-habitable-rooms",
"mainheat-description"
]
]
asset_list = asset_list.merge(
epc_df,
how="left",
left_on=["ADDRESS"],
right_on=["asset_list_address"]
)
asset_list = asset_list.drop(columns=["asset_list_address"])
# Rename the columns
asset_list = asset_list.rename(columns={
"inspection-date": "Date of last EPC",
"current-energy-efficiency": "SAP score on register",
"current-energy-rating": "EPC rating on register",
"property-type": "Property Type",
"built-form": "Archetype",
"total-floor-area": "Property Floor Area",
"construction-age-band": "Property Age Band",
"floor-height": "Property Floor Height",
"number-habitable-rooms": "Number of Habitable Rooms",
"walls-description": "Wall Construction",
"roof-description": "Roof Construction",
"mainheat-description": "Heating Type",
"secondheat-description": "Secondary Heating",
"transaction-type": "Reason for last EPC"
})
asset_list["Estimated Number of Floors"] = asset_list.apply(
lambda x: estimate_number_of_floors(property_type=x["Property Type"]), axis=1
)
asset_list["Property Floor Area"] = asset_list["Property Floor Area"].astype(float)
asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].astype(float)
asset_list["Estimated Perimeter (m)"] = asset_list.apply(
lambda x: estimate_perimeter(
floor_area=x["Property Floor Area"] / x["Estimated Number of Floors"],
num_rooms=x["Number of Habitable Rooms"] / x["Estimated Number of Floors"],
), axis=1
)
asset_list["Estimated Heat Loss Perimeter (m)"] = asset_list.apply(
lambda x: estimate_external_wall_area(
num_floors=x["Estimated Number of Floors"],
floor_height=float(x["Property Floor Height"]) if x["Property Floor Height"] else 2.5,
perimeter=x["Estimated Perimeter (m)"],
built_form=x["Archetype"]
),
axis=1
)
asset_list["Roof Insulation Thickness"] = asset_list.apply(
lambda x: RoofAttributes(description=x["Roof Construction"]).process()["insulation_thickness"],
axis=1
)
# Store as an excel
filename = "LHP EPC Data pull.xlsx"
asset_list.to_excel(filename, index=False)

43
etl/customers/northern_gorup/test_asset_list.py Normal file
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import pandas as pd
from utils.s3 import save_csv_to_s3
USER_ID = 8
PORTFOLIO_ID = 81
def app():
asset_list = [
{
'uprn': 10070868263,
"address": "Apartment 307, Flint Glass Wharf",
"postcode": "M4 6AD",
},
{
'uprn': 10070868244,
"address": "Apartment 106, Flint Glass Wharf",
"postcode": "M4 6AD",
}
]
asset_list = pd.DataFrame(asset_list)
# Store the asset list in s3
filename = f"{USER_ID}/{PORTFOLIO_ID}/pilot.csv"
save_csv_to_s3(
dataframe=asset_list,
bucket_name="retrofit-plan-inputs-dev",
file_name=filename
)
body = {
"portfolio_id": str(PORTFOLIO_ID),
"housing_type": "Private",
"goal": "Increase EPC",
"goal_value": "B",
"trigger_file_path": filename,
"already_installed_file_path": "",
"patches_file_path": "",
"non_invasive_recommendations_file_path": "",
"budget": None,
}
print(body)

156
etl/customers/places_for_people/EPC data pull - 12th June.py Normal file
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import os
import pandas as pd
from tqdm import tqdm
import numpy as np
from dotenv import load_dotenv
from backend.SearchEpc import SearchEpc
from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes
from recommendations.recommendation_utils import (
estimate_perimeter,
estimate_external_wall_area,
estimate_number_of_floors
)
load_dotenv(dotenv_path="backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
def app():
"""
This app is EPC pulling data for some properties owned by LHP
:return:
"""
asset_list = pd.read_excel(
"/Users/khalimconn-kowlessar/Downloads/Places for People NORTH WEST - EPC DATA PULL REQUEST.xlsx", header=0
)
epc_data = []
for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
full_address = home["Address"]
address1 = home["AddressLine1"]
postcode = home["Postcode"]
searcher = SearchEpc(
address1=address1,
postcode=postcode,
auth_token=EPC_AUTH_TOKEN,
os_api_key="",
property_type=None,
fast=True,
full_address=full_address
)
# Force the skipping of estimating the EPC
searcher.ordnance_survey_client.property_type = None
searcher.ordnance_survey_client.built_form = None
searcher.find_property(skip_os=True)
if searcher.newest_epc is None:
continue
epc = {
"asset_list_address": full_address,
**searcher.newest_epc.copy()
}
epc_data.append(epc)
epc_df = pd.DataFrame(epc_data)
# Retrieve just the data we need
epc_df = epc_df[
[
"asset_list_address",
"uprn",
"property-type",
"built-form",
"inspection-date",
"current-energy-rating",
"current-energy-efficiency",
"roof-description",
"walls-description",
"transaction-type",
# New fields needed
"secondheat-description",
"total-floor-area",
"construction-age-band",
"floor-height",
"number-habitable-rooms",
"mainheat-description"
]
]
# epc_df.to_csv("pfp sales data.csv", index=False)
asset_list = asset_list.merge(
epc_df,
how="left",
left_on=["Address"],
right_on=["asset_list_address"]
)
asset_list = asset_list.drop(columns=["asset_list_address"])
# Rename the columns
asset_list = asset_list.rename(columns={
"inspection-date": "Date of last EPC",
"current-energy-efficiency": "SAP score on register",
"current-energy-rating": "EPC rating on register",
"property-type": "EPC Property Type",
"built-form": "EPC Archetype",
"total-floor-area": "EPC Property Floor Area",
"construction-age-band": "EPC Property Age Band",
"floor-height": "EPC Property Floor Height",
"number-habitable-rooms": "EPC Number of Habitable Rooms",
"walls-description": "EPC Wall Construction",
"roof-description": "EPC Roof Construction",
"mainheat-description": "EPC Heating Type",
"secondheat-description": "EPC Secondary Heating",
"transaction-type": "Reason for last EPC"
})
asset_list["Estimated Number of Floors"] = asset_list.apply(
lambda x: estimate_number_of_floors(
property_type=x["EPC Property Type"]
) if not pd.isnull(x["EPC Property Type"]) else None, axis=1
)
asset_list["EPC Property Floor Area"] = asset_list["EPC Property Floor Area"].astype(float)
asset_list["EPC Number of Habitable Rooms"] = np.where(
asset_list["EPC Number of Habitable Rooms"] == "",
None,
asset_list["EPC Number of Habitable Rooms"]
)
asset_list["EPC Number of Habitable Rooms"] = asset_list["EPC Number of Habitable Rooms"].astype(float)
asset_list["Estimated Perimeter (m)"] = asset_list.apply(
lambda x: estimate_perimeter(
floor_area=x["EPC Property Floor Area"] / x["Estimated Number of Floors"],
num_rooms=x["EPC Number of Habitable Rooms"] / x["Estimated Number of Floors"],
), axis=1
)
asset_list["Estimated Heat Loss Perimeter (m)"] = asset_list.apply(
lambda x: estimate_external_wall_area(
num_floors=x["Estimated Number of Floors"],
floor_height=float(x["EPC Property Floor Height"]) if x["EPC Property Floor Height"] else 2.5,
perimeter=x["Estimated Perimeter (m)"],
built_form=x["EPC Archetype"]
),
axis=1
)
asset_list["Roof Insulation Thickness"] = asset_list.apply(
lambda x: RoofAttributes(description=x["EPC Roof Construction"]).process()[
"insulation_thickness"] if not pd.isnull(x["EPC Roof Construction"]) else None,
axis=1
)
# Store as an excel
filename = "Places for People NORTH WEST - EPC DATA PULL.xlsx"
asset_list.to_excel(filename, index=False)

164
etl/customers/places_for_people/parity_comparison.py Normal file
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"""
This script is used to pull together some case studies for the Parity Projects comparison
"""
import pandas as pd
from backend.SearchEpc import SearchEpc
from dotenv import load_dotenv
import os
load_dotenv("backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
parity_measures = pd.read_excel(
"/Users/khalimconn-kowlessar/Documents/hestia/Places For People/Parity Sample All Addresses and Measures.xlsx",
sheet_name="Total Measures"
)
solar_measures = parity_measures[parity_measures["Category"] == "SolarPV"]
example_1 = parity_measures[
parity_measures["Address Id (used by website)"] == 6125299
].copy()
config = {
"address": "14 Victoria Road",
"postcode": "BD20 8SY",
"uprn": 100050346517
}
# Point 1:
# Parity tends to re-score the EPCs, even if they're extrememly recent.
# For example for '14, Victoria Road, Cross Hills, KEIGHLEY, North Yorkshire, ENGLAND, BD20 8SY'
# The most recent EPC was done 15 May 2023, and landed at a 66D, however for some reason, parity re-score this
# home to be a 63.91. It's unclear why this is done
example_1_measures = example_1[["MeasureGroupName", "Individual SAP increase"]].copy()
# - LEDS: 0.25 SAP points
# - 300mm of loft insulation from 200mm: 0.43 SAP points - where is this deduced from? Since the latest survey
# indicates 250mm insulation in place
# - Check construction of unknown party wall and fill cavity if appropriate: 0.12 SAP points (highly speculative,
# not based on any data)
# - Block open chimneys: 1.61 SAP points - latest survey showed 0 open fireplaces
# - ASHP (45 degree emitters) with enhanced existing radiator central heating and hot water, from E rated gas boiler
# 6.38 SAP points
# - 4kWp PV array south and 30 degree pitch with no shading: 30.24 SAP points
# Notes on solar - 30.34 seems like a lot
# 400 watt is the solar panel output
# Let's do a test for this property
# This would be 10 solar panels
# Using typical solar panel dimensions, this would be 19.63555m2 of roof space
# The area of the roof is between 60 - 64.5 m2 (we use a API to get the roof data), implying only
# around 30% of the roof is covered by solar panels
# Using our machine learning model to simulate the impact of this on SAP, this would more likely result in
# a
from utils.s3 import read_dataframe_from_s3_parquet
training_data = read_dataframe_from_s3_parquet(
bucket_name="retrofit-data-dev",
file_key="sap_change_model/2024-06-09-10-36-53/dataset_rooms.parquet"
)
# Look for properties where the only difference is solar
ending_cols = [
c for c in training_data.columns if "_ending" in c and "photo_supply" not in c
]
ending_cols = [
c for c in ending_cols if
c not in ["sap_ending", "heat_demand_ending", "carbon_ending", "transaction_type_ending", "days_to_ending"]
]
column_pairs = {}
for col in ending_cols:
starting = col.split("_ending")[0]
if starting + "_starting" in training_data.columns:
starting_col = starting + "_starting"
else:
starting_col = starting
column_pairs[col] = starting_col
filtered = training_data.copy()
# Take rows that had solar installs
filtered = filtered[filtered["photo_supply_ending"] != filtered["photo_supply_starting"]]
for ending_col, starting_col in column_pairs.items():
filtered = filtered[filtered[ending_col] == filtered[starting_col]]
print(f"ending_col: {ending_col}, filtered shape: {filtered.shape}")
avg_change = filtered.groupby("photo_supply_ending")["rdsap_change"].mean().reset_index()
# I've take every single case of there being two EPCs for a property, where the only difference between the first
# and second is the solar installation. This is 2692 properties, across the UK. In only 4 instances has this resulted in
# 30 or more SAP points
# Some functions based on the SAP methodology:
import numpy as np
total_floor_area = 50
occupants = calculate_occupants(total_floor_area)
appliances_energy_use = estimate_electrical_appliances(occupants, total_floor_area)
cooking_energy_use = estimate_cooking(occupants)
def calculate_occupants(total_floor_area):
"""
From Table 1b
:param total_floor_area:
:return:
"""
return 1 + (1.76 * (1 - np.exp(-0.000349 * (total_floor_area - 13.9) * (total_floor_area - 13.9))) + 0.0013 * (
total_floor_area - 13.9))
def estimate_electrical_appliances(occupants, total_floor_area):
"""
From seciont L2 Electrical appliances
:param occupants:
:param total_floor_area:
:return:
"""
e_a = 207.8 * np.power(total_floor_area * occupants, 0.4717)
days_in_month = {
1: 31,
2: 28,
3: 31,
4: 30,
5: 31,
6: 30,
7: 31,
8: 31,
9: 30,
10: 31,
11: 30,
12: 31
}
eam = 0
for m in range(1, 13):
nm = days_in_month[m]
eam += e_a * (1 + 0.157 * np.cos(2 * np.pi * (m - 1.78) / 12)) * nm / 365
return eam
def estimate_cooking(occupants):
"""
From section L3 Cooking
:param occupants:
:return:
"""
return 35 + 7 * occupants
primary_energy_per_m2 = 288 # kWh/m2 per year
primary_energy_regulated = primary_energy_per_m2 * total_floor_area
primary_energy_factor_electricity = 1.1 # Example factor
primary_energy_appliances = appliances_energy_use * primary_energy_factor_electricity
primary_energy_cooking = cooking_energy_use * primary_energy_factor_electricity * 365 # Annualize cooking energy
total_primary_energy_use = primary_energy_regulated + primary_energy_appliances

43
etl/customers/places_for_people/route_march.py
View file

@ -295,6 +295,49 @@ def main():
addresses_df2.to_excel("Places For People EPC data with surveyor.xlsx", index=False)
# Read in
df = pd.read_excel("Places For People EPC data with surveyor.xlsx")
df = df[
df["assessor_name"].isin(
[
"Arsalan Khalid", "Kieran Bradnock", "Wayne Davies", "Lindsay Sands", "Bruce Nethercot",
"Christopher Hearn", "Robert Sigerson", "Daniel Riddle", "Leroy Sands",
]
)
]
# Get the EPC
heights = []
for _, row in tqdm(df.iterrows(), total=len(df)):
searcher = SearchEpc(
address1=str(row["Matched EPC Address"]),
postcode=str(row["POSTCODE"]),
uprn=str(int(row["uprn"])),
auth_token=EPC_AUTH_TOKEN,
os_api_key="",
property_type=None,
fast=True,
)
# Force the skipping of estimating the EPC
searcher.ordnance_survey_client.property_type = None
searcher.ordnance_survey_client.built_form = None
searcher.find_property(skip_os=True)
height = {
"uprn": row["uprn"],
"floor_height": searcher.newest_epc["floor-height"]
}
heights.append(height)
df = df.merge(
pd.DataFrame(heights),
how="left",
on="uprn"
)
df.to_excel("WF surveyors with floor heights.xlsx", index=False)
if __name__ == "__main__":
main()

165
etl/customers/stonewater/no_matches.py Normal file
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no_matches = [
{
'internal_id': 4626, 'full_address': '1 Dean Lane, Sixpenny Handley, Salisbury, SP5 5AS', 'postcode': 'SP5 5AS',
'Note': 'No match found - all addresses in this postcode are for Mulberry Court, Sixpenny Handley, Salisbury, '
'SP5 5AS, addresses not recognised by Zoopla - possibly the postcode is incorrect and this could be'
'Handley Enterprises Ltd, Unit 1 Dean Lane, Sixpenny Handley, Salisbury, SP5 5PA.'
'Or this could be 1 Mulberry Court Sixpenny Handley, Salisbury SP5 5AS'
},
{
'internal_id': 4627, 'full_address': '3 Dean Lane, Sixpenny Handley, Salisbury, SP5 5AS', 'postcode': 'SP5 5AS',
'Note': 'No match found - all addresses in this postcode are for Mulberry Court, Sixpenny Handley, Salisbury, '
'SP5 5AS, addresses not recognised by Zoopla - possibly the postcode is incorrect and this could be'
'2 Town Farm House, Dean Lane, Sixpenny Handley, Salisbury, SP5 5PA'
'Or this could be 3 Mulberry Court Sixpenny Handley, Salisbury SP5 5AS'
},
{
'internal_id': 4628, 'full_address': '5 Dean Lane, Sixpenny Handley, Salisbury, SP5 5AS', 'postcode': 'SP5 5AS',
'Note': 'No match found - all addresses in this postcode are for Mulberry Court, Sixpenny Handley, Salisbury, '
'SP5 5AS, addresses not recognised by Zoopla - possibly the postcode is incorrect and this could be'
'4 Town Farm House, Dean Lane, Sixpenny Handley, Salisbury, SP5 5PA'
'Or this could be 5 Mulberry Court Sixpenny Handley, Salisbury SP5 5AS'
},
{
'internal_id': 544, 'full_address': 'Room 1, Sawr, PO Box 1354, Bedford, MK41 5AB', 'postcode': 'MK41 5AB',
"Note": "Postcode deleted in April 2024: https://checkmypostcode.uk/mk415ab"
},
{
'internal_id': 5116, 'full_address': '3 Huntspond Road, Titchfield, Fareham, PO14 4SS', 'postcode': 'PO14 4SS',
'Note': 'Is this 3 St Francis Court, 195 Hunts Pond Road, Fareham, PO14 4SS, uprn: 100061988896'
},
{
'internal_id': 5114, 'full_address': '4 Huntspond Road, Titchfield, Fareham, PO14 4SS', 'postcode': 'PO14 4SS',
'Note': 'Is this 4 St Francis Court, 195 Hunts Pond Road, Fareham, PO14 4SS, uprn: 100061988897'
},
{
'internal_id': 5115, 'full_address': '2 Huntspond Road, Titchfield, Fareham, PO14 4SS', 'postcode': 'PO14 4SS',
'Note': 'Is this 2 St Francis Court, 195 Hunts Pond Road, Fareham, PO14 4SS, uprn: 100061988895'
},
{
'internal_id': 5113, 'full_address': '6 Huntspond Road, Titchfield, Fareham, PO14 4SS', 'postcode': 'PO14 4SS',
'Note': 'Is this 6 St Francis Court, 195 Hunts Pond Road, Fareham, PO14 4SS, uprn: 100061988899'
},
{
'internal_id': 5112, 'full_address': '1 Huntspond Road, Titchfield, Fareham, PO14 4SS', 'postcode': 'PO14 4SS',
'Note': 'Is this 1 St Francis Court, 195 Hunts Pond Road, Fareham, PO14 4SS, uprn: 100061988894'
},
{
'internal_id': 3846, 'full_address': '2 Beaufort Road, Southbourne, Bournemouth, BH6 5BD',
'postcode': 'BH6 5BD',
'Note': "2 Beaufort Road, Southbourne, Bournemouth is listed under the postcode BH6 5AL - is there a typo in "
"the postcode?"
},
{
'internal_id': 4497, 'full_address': '11 Brokenford Lane, Totton, Southampton, SO40 9LZ',
'postcode': 'SO40 9LZ',
'Note': "This postcode doesn't appear to exist, closest is 10 brokenford lane, Totton, Southampton, SO40 9DW."
"What should this be?"
},
{
'internal_id': 4181, 'full_address': '25a Eastcott Road, Old Town, Swindon, SN1 3PA', 'postcode': 'SN1 3PA',
'Note': 'All addresses at this postcode are for Bow Court. '
'Closest match is 25 Eastcott Road, Swindon, SN1 3LT, but there is no 25A'
},
{
'internal_id': 5447, 'full_address': '3 Send Road, Send Road, Reading, RG4 8EP', 'postcode': 'RG4 8EP',
"Note": "These is no 'Send Road' at this postcode. There are a few possible matches, e.g. Flat 3, "
"1 Send Road, RG4 8EH"
},
{
'internal_id': 5449, 'full_address': '5 Send Road, Send Road, Reading, RG4 8EP', 'postcode': 'RG4 8EP',
"Note": "Same as for 3 Send Road"
},
{
'internal_id': 5450, 'full_address': '6 Send Road, Send Road, Reading, RG4 8EP', 'postcode': 'RG4 8EP',
"Note": "Same as for 3 Send Road"
},
{
'internal_id': 5446, 'full_address': '1 Send Road, Send Road, Reading, RG4 8EP', 'postcode': 'RG4 8EP',
"Note": "Same as for 3 Send Road"
},
{
'internal_id': 5448, 'full_address': '4 Send Road, Send Road, Reading, RG4 8EP', 'postcode': 'RG4 8EP',
"Note": "Same as for 3 Send Road"
},
{
'internal_id': 5451, 'full_address': '7 Send Road, Send Road, Reading, RG4 8EP', 'postcode': 'RG4 8EP',
"Note": "Same as for 3 Send Road"
},
{
'internal_id': 4547, 'full_address': '2 Cecil Terrace, Bemerton, Salisbury, SP2 9NE', 'postcode': 'SP2 9NE',
"Note": "Addresses for this postcode are for The Croft, SP2 9NE. Should this be 2 Cecil Terrace SP2 9ND, with"
"uprn: 100121039798 ?"
},
{
'internal_id': 4549, 'full_address': '4 Cecil Terrace, Bemerton, Salisbury, SP2 9NE', 'postcode': 'SP2 9NE',
"Note": "Addresses for this postcode are for The Croft, SP2 9NE. Should this be 4 Cecil Terrace SP2 9ND?"
},
{
'internal_id': 3601, 'full_address': '20 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should this be 20 Constitution Hill Gardens, Poole, BH14 0PY? (i.e. postcode is wrong) "
"uprn: 10001086693"
},
{
'internal_id': 3592, 'full_address': '7 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"
},
{
'internal_id': 3594, 'full_address': '9 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"
},
{
'internal_id': 3591, 'full_address': '6 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"
},
{
'internal_id': 3593, 'full_address': '8 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{
'internal_id': 3590, 'full_address': '5 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{
'internal_id': 3589, 'full_address': '3 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{
'internal_id': 3600, 'full_address': '18 Constitution Hill, Parkstone, Poole, BH14 0PX',
'postcode': 'BH14 0PX', "Note": "Should the postcode be BH14 0PY ?"},
{
'internal_id': 3599, 'full_address': '17 Constitution Hill, Parkstone, Poole, BH14 0PX',
'postcode': 'BH14 0PX', "Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3598, 'full_address': '15 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3608, 'full_address': '26 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3610, 'full_address': '30 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3603, 'full_address': '22 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3612, 'full_address': '32 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3595, 'full_address': '10 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3613, 'full_address': '34 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0PY ?"},
{'internal_id': 3597, 'full_address': '12 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3602, 'full_address': '21 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3606, 'full_address': '19 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3604, 'full_address': '23 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3605, 'full_address': '25 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3609, 'full_address': '29 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3596, 'full_address': '11 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3607, 'full_address': '27 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 3611, 'full_address': '31 Constitution Hill, Parkstone, Poole, BH14 0PX', 'postcode': 'BH14 0PX',
"Note": "Should the postcode be BH14 0QB ?"},
{'internal_id': 5622, 'full_address': '26 Roman Way, Andover, SP10 5HZ', 'postcode': 'SP10 5HZ',
'Note': 'Shoul this postcode be SP10 5JU ?'}
]

1917
etl/customers/stonewater/shdf_3_clustering.py Normal file
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148
etl/customers/unitas/20_may_2024_data_pull.py Normal file
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@ -0,0 +1,148 @@
import os
import pandas as pd
from tqdm import tqdm
from dotenv import load_dotenv
from backend.SearchEpc import SearchEpc
from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes
from recommendations.recommendation_utils import (
estimate_perimeter,
estimate_external_wall_area,
estimate_number_of_floors
)
load_dotenv(dotenv_path="backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
def app():
"""
This app is EPC pulling data for some properties owned by Unitas
:return:
"""
# asset_list = read_excel_from_s3(
# bucket_name="retrofit-datalake-dev",
# file_key="customers/guiness/TGP CW Properties PV.xlsx",
# header_row=0
# )
asset_list = pd.read_excel(
"/Users/khalimconn-kowlessar/Downloads/UNITAS BUNGALOWS - EPC DATA PULL.xlsx", header=0
)
epc_data = []
for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
searcher = SearchEpc(
address1=str(home["Address Line 1"]),
postcode=home["Post Code"],
uprn=home["Property Reference"],
auth_token=EPC_AUTH_TOKEN,
os_api_key="",
property_type=None,
fast=True
)
# Force the skipping of estimating the EPC
searcher.ordnance_survey_client.property_type = None
searcher.ordnance_survey_client.built_form = None
searcher.find_property(skip_os=True)
if searcher.newest_epc is None:
continue
epc = {
"asset_list_address": home["Address Line 1"],
"asset_list_postcode": home["Post Code"],
**searcher.newest_epc.copy()
}
epc_data.append(epc)
epc_df = pd.DataFrame(epc_data)
# Retrieve just the data we need
epc_df = epc_df[
[
"asset_list_address",
"uprn",
"property-type",
"built-form",
"inspection-date",
"current-energy-rating",
"current-energy-efficiency",
"roof-description",
"walls-description",
"transaction-type",
# New fields needed
"secondheat-description",
"total-floor-area",
"construction-age-band",
"floor-height",
"number-habitable-rooms",
"mainheat-description"
]
]
asset_list = asset_list.merge(
epc_df,
how="left",
left_on=["Address Line 1"],
right_on=["asset_list_address"]
)
asset_list = asset_list.drop(columns=["asset_list_address"])
# Rename the columns
asset_list = asset_list.rename(columns={
"inspection-date": "Date of last EPC",
"current-energy-efficiency": "SAP score on register",
"current-energy-rating": "EPC rating on register",
"property-type": "EPC Property Type",
"built-form": "Archetype",
"total-floor-area": "Property Floor Area",
"construction-age-band": "Property Age Band",
"floor-height": "Property Floor Height",
"number-habitable-rooms": "Number of Habitable Rooms",
"walls-description": "Wall Construction",
"roof-description": "Roof Construction",
"mainheat-description": "Heating Type",
"secondheat-description": "Secondary Heating",
"transaction-type": "Reason for last EPC"
})
asset_list["Estimated Number of Floors"] = asset_list.apply(
lambda x: estimate_number_of_floors(property_type=x["EPC Property Type"]) if not pd.isnull(
x["EPC Property Type"]) else None,
axis=1
)
asset_list["Property Floor Area"] = asset_list["Property Floor Area"].astype(float)
asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].astype(float)
asset_list["Estimated Perimeter (m)"] = asset_list.apply(
lambda x: estimate_perimeter(
floor_area=x["Property Floor Area"] / x["Estimated Number of Floors"],
num_rooms=x["Number of Habitable Rooms"] / x["Estimated Number of Floors"],
) if not pd.isnull(x["uprn"]) else None, axis=1
)
asset_list["Estimated Heat Loss Perimeter (m)"] = asset_list.apply(
lambda x: estimate_external_wall_area(
num_floors=x["Estimated Number of Floors"],
floor_height=float(x["Property Floor Height"]) if x["Property Floor Height"] else 2.5,
perimeter=x["Estimated Perimeter (m)"],
built_form=x["Archetype"]
) if not pd.isnull(x["uprn"]) else None,
axis=1
)
asset_list["Roof Insulation Thickness"] = asset_list.apply(
lambda x: RoofAttributes(description=x["Roof Construction"]).process()["insulation_thickness"] if not pd.isnull(
x["uprn"]) else None,
axis=1
)
# Store as an excel
filename = "UNITAS BUNGALOWS - EPC DATA PULL - May 30tg 2024.xlsx"
asset_list.to_excel(filename, index=False)

182
etl/customers/unitas/Audit_check.py Normal file
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@ -0,0 +1,182 @@
import pandas as pd
import os
from tqdm import tqdm
from dotenv import load_dotenv
from backend.SearchEpc import SearchEpc
load_dotenv(dotenv_path="backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
def app():
# Read in rolling master
master = pd.read_csv(
"/Users/khalimconn-kowlessar/Downloads/UNITAS ( STOKE) MASTER ROLLING SHEET UPDATED 16.5.24 K - PASSWORD "
"PROTECTED/ECO 4 - PHASE 1-Table 1.csv"
)
master = master[master["INSTALLER"] == "SCIS"]
master = master[
[
'UPRN', 'NO.', 'Street / Block Name', 'Town/Area', 'Post Code', 'Surveyor', "SUBMISSION DATE"
]
]
master = master[~pd.isnull(master["UPRN"])]
master = master[master["UPRN"] != "NOT ON ASSET LIST"]
heights = []
eco_assessment_epcs = []
for _, row in tqdm(master.iterrows(), total=len(master)):
searcher = SearchEpc(
address1="",
postcode="",
uprn=str(int(row["UPRN"])),
auth_token=EPC_AUTH_TOKEN,
os_api_key="",
property_type=None,
fast=False,
)
# Force the skipping of estimating the EPC
searcher.ordnance_survey_client.property_type = None
searcher.ordnance_survey_client.built_form = None
searcher.find_property(skip_os=True)
if searcher.newest_epc is None:
continue
# Look for eco assessment epcs
eco_epc = [x for x in [searcher.newest_epc] + searcher.older_epcs if x['transaction-type'] == 'ECO assessment']
# Take the newest
eco_epc = sorted(eco_epc, key=lambda x: x['inspection-date'], reverse=True)
if eco_epc:
eco_assessment_epcs.append(eco_epc[0])
height = {
"uprn": row["UPRN"],
"floor_height": searcher.newest_epc["floor-height"]
}
heights.append(height)
heights_df = pd.DataFrame(heights)
eco_assessment_epcs_df = pd.DataFrame(eco_assessment_epcs)
merged_heights_df = master.merge(heights_df, left_on="UPRN", right_on="uprn", how="inner")
merged_heights_df = merged_heights_df[merged_heights_df["floor_height"] != ""]
merged_eco_assessment_epcs_df = master.merge(eco_assessment_epcs_df[["uprn", "floor-height"]], left_on="UPRN",
right_on="uprn", how="inner")
merged_eco_assessment_epcs_df["floor-height"] = merged_eco_assessment_epcs_df["floor-height"].astype(float)
merged_eco_assessment_epcs_df.groupby("Surveyor")["floor-height"].mean()
# Store
merged_heights_df.to_csv("Unitas 2022 heights - based on newest EPC.csv", index=False)
merged_eco_assessment_epcs_df.to_csv("Unitas 2022 heights - based on ECO assessment EPC.csv", index=False)
# Read in a diferent sheet
master = pd.read_excel(
"/Users/khalimconn-kowlessar/Downloads/COMMUNITY HOUSING SURVEYS WITH A POST EPC.xlsx"
)
master["row_number"] = master.index
heights = []
eco_assessment_epcs = []
expected_pre = []
expected_post = []
biggest_floor_height = []
for _, row in tqdm(master.iterrows(), total=len(master)):
full_address = ", ".join([
str(row["NO."]), row["Street / Block Name"], row["Town/Area"], row["Post Code"]
])
searcher = SearchEpc(
address1=str(row["NO."]),
postcode=str(row["Post Code"]),
auth_token=EPC_AUTH_TOKEN,
os_api_key="",
property_type=None,
fast=False,
full_address=full_address
)
# Force the skipping of estimating the EPC
searcher.ordnance_survey_client.property_type = None
searcher.ordnance_survey_client.built_form = None
searcher.find_property(skip_os=True)
if searcher.newest_epc is None:
continue
all_epcs = [searcher.newest_epc] + searcher.older_epcs
# Search for SAP 54s
sap_54s = [x for x in all_epcs if x["current-energy-efficiency"] == "54"]
sap_69s = [x for x in all_epcs if x["current-energy-efficiency"] == "69"]
heights = [float(x["floor-height"]) for x in all_epcs if x["floor-height"] != ""]
# Look for eco assessment epcs
eco_epc = [x for x in [searcher.newest_epc] + searcher.older_epcs if x['transaction-type'] == 'ECO assessment']
# Take the newest
eco_epc = sorted(eco_epc, key=lambda x: x['inspection-date'], reverse=True)
if eco_epc:
eco_assessment_epcs.append(
{
"row_number": row["row_number"],
**eco_epc[0]
}
)
if heights:
floor_height_max = max(heights)
biggest_floor_height.append(
{
"row_number": row["row_number"],
"floor_height": floor_height_max
}
)
if sap_54s:
expected_pre.append(
{
"row_number": row["row_number"],
**sap_54s[0]
}
)
if sap_69s:
expected_post.append(
{
"row_number": row["row_number"],
**sap_69s[0]
}
)
expected_pre_df = pd.DataFrame(expected_pre)
expected_post_df = pd.DataFrame(expected_post)
heights_df = pd.DataFrame(biggest_floor_height)
eco_assessment_epcs_df = pd.DataFrame(eco_assessment_epcs)
merged_heights_df = master.merge(heights_df, on="row_number", how="inner")
merged_heights_df = merged_heights_df[merged_heights_df["floor_height"] != ""]
merged_eco_assessment_epcs_df = master.merge(
eco_assessment_epcs_df[["row_number", "floor-height"]], on="row_number", how="inner"
)
merged_eco_assessment_epcs_df["floor-height"] = merged_eco_assessment_epcs_df["floor-height"].astype(float)
merged_eco_assessment_epcs_df.groupby("Surveyor")["floor-height"].mean()
# Check average floor height for social housing properties with ECO assessment EPCs in Birmingham
sample = pd.read_csv("local_data/all-domestic-certificates/domestic-E08000025-Birmingham/certificates.csv")
sample = sample[sample["TRANSACTION_TYPE"] == "ECO assessment"]
sample = sample[sample["TENURE"].isin(["rental (social)", "Rented (social)"])]
sample["FLOOR_HEIGHT"] = sample["FLOOR_HEIGHT"].astype(float)
sample["FLOOR_HEIGHT"].mean()
sample[pd.to_datetime(sample["LODGEMENT_DATE"]) >= "2022-01-01"]["FLOOR_HEIGHT"].mean()

18
etl/epc/Dataset.py
View file

@ -203,11 +203,11 @@ class TrainingDataset(BaseDataset):
common_cols = [[col + "_starting", col + "_ending"] for col in common_cols]
self.df = self.df.loc[
:,
no_suffix_cols
+ only_ending_cols
+ [col for cols in common_cols for col in cols],
]
:,
no_suffix_cols
+ only_ending_cols
+ [col for cols in common_cols for col in cols],
]
def _remove_abnormal_change_in_floor_area(self):
"""
@ -511,7 +511,7 @@ class TrainingDataset(BaseDataset):
expanded_df["is_sandstone_or_limestone"]
== expanded_df["is_sandstone_or_limestone_ending"]
)
]
]
elif component == "floor":
expanded_df = expanded_df[
(expanded_df["is_suspended"] == expanded_df["is_suspended_ending"])
@ -528,7 +528,7 @@ class TrainingDataset(BaseDataset):
expanded_df["is_to_external_air"]
== expanded_df["is_to_external_air_ending"]
)
]
]
elif component == "roof":
expanded_df = expanded_df[
(expanded_df["is_pitched"] == expanded_df["is_pitched_ending"])
@ -541,7 +541,7 @@ class TrainingDataset(BaseDataset):
expanded_df["has_dwelling_above"]
== expanded_df["has_dwelling_above_ending"]
)
]
]
return expanded_df
@ -742,7 +742,7 @@ class TrainingDataset(BaseDataset):
self.df[col] = self.df[col].fillna("Unknown")
def _null_validation(self, information: str):
print(f"Null validation after {information}")
# print(f"Null validation after {information}")
if pd.isnull(self.df).sum().sum():
raise ValueError(f"Null values found in dataset, after step {information}")

2
etl/epc_clean/epc_attributes/RoofAttributes.py
View file

@ -45,7 +45,7 @@ class RoofAttributes(Definitions):
"""
self.description: str = description.lower().strip()
self.nodata = not description or description in self.DATA_ANOMALY_MATCHES
self.nodata = not description or description in self.DATA_ANOMALY_MATCHES or self.description == "sap05:roof"
self.welsh_translation_search()

7
etl/property_valuation/requirements.txt Normal file
View file

@ -0,0 +1,7 @@
seleniumbase
beautifulsoup4
requests
pandas
tqdm
openpyxl
undetected_chromedriver

88
etl/property_valuation/scrape_valuations.py Normal file
View file

@ -0,0 +1,88 @@
import requests
import random
import time
import pandas as pd
from bs4 import BeautifulSoup
from tqdm import tqdm
from seleniumbase import Driver
from seleniumbase import page_actions
import undetected_chromedriver as webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
import time
import pandas as pd
BASE_URL = "https://www.zoopla.co.uk/property/uprn/{uprn}/"
def initialize_driver():
driver = Driver(headless=True, uc=True) # Set headless to True if you want headless mode
return driver
def app():
# Read in the starting asset list
asset_list = pd.read_excel("portfolio_epc_data_50m 28th May.xlsx")
asset_list = asset_list[["UPRN", "ADDRESS", "POSTCODE"]]
# asset_list.to_excel("property value.xlsx", index=False)
# Generate the list of urls
urls = [BASE_URL.format(uprn=uprn) for uprn in asset_list["UPRN"]]
driver = webdriver.Chrome()
driver = initialize_driver()
driver.set_page_load_timeout(30) # Increase page load timeout
result = []
for i, (url, uprn) in tqdm(enumerate(zip(urls, asset_list["UPRN"].tolist())), total=len(urls)):
# Every 10 requests sleep for an extra 7 seconds
if len(result) % 10 == 0 and len(result) != 0:
time.sleep(7)
try:
driver.get(url)
page_actions.wait_for_element_visible(driver, "p[data-testid='estimate-blurred']", timeout=30)
price_element = driver.find_element("css selector", "p[data-testid='estimate-blurred']")
price = price_element.get_text(strip=True)
low_price_element = driver.find_element("css selector", "span[data-testid='low-estimate-blurred']")
low_price = low_price_element.get_text(strip=True)
high_price_element = driver.find_element("css selector", "span[data-testid='high-estimate-blurred']")
high_price = high_price_element.get_text(strip=True)
result.append(
{
"UPRN": uprn,
"price": price,
"lower_estimate": low_price,
"upper_estimate": high_price
}
)
# Sleep a random amount of time between 5 and 20 seconds
sleep_time = 5 + (15 * random.random())
time.sleep(sleep_time)
except Exception as e:
print(f"Failed to retrieve data for UPRN {uprn} at iteration {i}: {e}")
# Store the result depending on where we are
savepoint = pd.DataFrame(result)
savepoint.to_csv(f"savepoint_index_{i}.csv", index=False)
# TODO: Testing Jina AI - didn't work but maybe one of the alternatives might work:
# https://www.youtube.com/watch?v=QxHE4af5BQE
response = requests.get("https://r.jina.ai/https://www.zoopla.co.uk/property/uprn/41222761/")
response.text
if __name__ == "__main__":
app()

30
recommendations/Costs.py
View file

@ -20,21 +20,21 @@ regional_labour_variations = [
# This data is based on the MCS database
MCS_SOLAR_PV_COST_DATA = {
"last_updated": "2024-01-04",
"average_cost_per_kwh": 2013.94,
"average_cost_per_kwh-Outer London": 2618.75,
"average_cost_per_kwh-Inner London": 2618.75,
"average_cost_per_kwh-South East England": 2083.33,
"average_cost_per_kwh-South West England": 2113,
"average_cost_per_kwh-East of England": 1973.86,
"average_cost_per_kwh-East Midlands": 1981.86,
"average_cost_per_kwh-West Midlands": 1926.55,
"average_cost_per_kwh-North East England": 2028.49,
"average_cost_per_kwh-North West England": 1620.42,
"average_cost_per_kwh-Yorkshire and the Humber": 2060.9,
"average_cost_per_kwh-Wales": 1898.83,
"average_cost_per_kwh-Scotland": 1967.97,
"average_cost_per_kwh-Northern Ireland": 2126.09,
"last_updated": "2024-06-10",
"average_cost_per_kwh": 1750,
"average_cost_per_kwh-Outer London": 1776,
"average_cost_per_kwh-Inner London": 1776,
"average_cost_per_kwh-South East England": 1672,
"average_cost_per_kwh-South West England": 1732,
"average_cost_per_kwh-East of England": 1721,
"average_cost_per_kwh-East Midlands": 1730,
"average_cost_per_kwh-West Midlands": 1761,
"average_cost_per_kwh-North East England": 1669,
"average_cost_per_kwh-North West England": 1764,
"average_cost_per_kwh-Yorkshire and the Humber": 1705,
"average_cost_per_kwh-Wales": 1896,
"average_cost_per_kwh-Scotland": 1767,
"average_cost_per_kwh-Northern Ireland": 1767,
}
# This data is based on the MCS database, We use the larger figure between the 2023 and 2024 average,

86
recommendations/HeatingRecommender.py
View file

@ -8,6 +8,14 @@ from recommendations.HeatingControlRecommender import HeatingControlRecommender
class HeatingRecommender:
ELECTRIC_HEATING_DESCRIPTIONS = [
"Room heaters, electric",
"Electric storage heaters",
"Electric storage heaters, radiators",
"Portable electric heaters assumed for most rooms",
]
high_heat_retention_contols_desc = "Controls for high heat retention storage heaters"
def __init__(self, property_instance: Property):
self.property = property_instance
@ -16,6 +24,24 @@ class HeatingRecommender:
self.heating_recommendations = []
self.heating_control_recommendations = []
self.has_electric_heating_description = (
self.property.main_heating["clean_description"] in self.ELECTRIC_HEATING_DESCRIPTIONS
)
def is_high_heat_retention_valid(self):
"""
Check conditions if high heat retention storage is valid
:return:
"""
# If the property has assumed electric heating, regardless of whether or not it has a mains connection, we
# can consider hhr storage heaters
electric_heating_assumed = (
self.property.main_heating["clean_description"] in ["No system present, electric heaters assumed"]
)
return self.has_electric_heating_description or electric_heating_assumed
def recommend(self, has_cavity_or_loft_recommendations, phase=0):
"""
Produces heating recommendations
@ -34,17 +60,10 @@ class HeatingRecommender:
# This first iteration of the recommender will provide very basic recommendation
# We recommend heating controls based on the main heating system
has_electric_heating_description = self.property.main_heating["clean_description"] in [
"Room heaters, electric", "Electric storage heaters", "Electric storage heaters, radiators"
]
no_heating_no_mains = (
self.property.main_heating["clean_description"] in ["No system present, electric heaters assumed"] and
not self.property.data["mains-gas-flag"]
)
if has_electric_heating_description or no_heating_no_mains:
if self.is_high_heat_retention_valid():
# Recommend high heat retention storage heaters
# TODO: We need to allow for the possibility that the property aleady has storage heaters, but just
# needs the controls
self.recommend_hhr_storage_heaters(phase=phase, system_change=True, heating_controls_only=False)
# if the property has mains heating with boiler and radiators, we recommend optimal heating controls
@ -61,7 +80,7 @@ class HeatingRecommender:
)
# We also check if the property has electric heating, but it has access to the mains gas
electic_heating_has_mains = has_electric_heating_description and self.property.data["mains-gas-flag"]
electic_heating_has_mains = self.has_electric_heating_description and self.property.data["mains-gas-flag"]
portable_heaters_has_mains = (
self.property.main_heating["clean_description"] in ["Portable electric heaters assumed for most rooms"] and
@ -93,16 +112,19 @@ class HeatingRecommender:
# In the future, we'll allow overrides, so that non-intrusive surveys can contradict these conditions
# and either allow or prevent the recommendation of an air source heat pump
suitable_property_type = self.property.data["property-type"] in ["House", "Bungalow"]
has_air_source_heat_pump = self.property.main_heating["has_air_source_heat_pump"]
if suitable_property_type and not has_air_source_heat_pump:
if self.is_ashp_valid():
self.recommend_air_source_heat_pump(
phase=phase, has_cavity_or_loft_recommendations=has_cavity_or_loft_recommendations
)
return
def is_ashp_valid(self):
suitable_property_type = self.property.data["property-type"] in ["House", "Bungalow"]
has_air_source_heat_pump = self.property.main_heating["has_air_source_heat_pump"]
return suitable_property_type and not has_air_source_heat_pump
def recommend_air_source_heat_pump(self, phase, has_cavity_or_loft_recommendations, _return=False):
"""
This method will implement the recommendation for an air source heat pump
@ -314,6 +336,27 @@ class HeatingRecommender:
return output
def is_hhr_already_installed(self):
"""
Check if the property already has high heat retention storage heaters
:return:
"""
already_has_hhr = "Electric storage heaters" in self.property.main_heating["clean_description"]
# Some electric storage heaters will show that the controls are "Manual charge controls" which are indicative
# of the old model of electric storage heaters, originating from 1961.
# Newer HHR storage heaters will charge up over night but will retain the heat durin the day for when warmth
# is actually needed, unlike traditional storage heaters that charge up at night and release heat during the day
# which isn't always ideal for the occupants.
already_has_hhr_contols = (
self.property.main_heating_controls[
"clean_description"
].lower() == self.high_heat_retention_contols_desc.lower()
)
return already_has_hhr and already_has_hhr_contols
def recommend_hhr_storage_heaters(self, phase, system_change, heating_controls_only, _return=False):
"""
We will recommend upgrading to a high heat retention storage system, if the current system is not already
@ -330,19 +373,14 @@ class HeatingRecommender:
controls_recommender = HeatingControlRecommender(self.property)
# The heating controls we're recommending for are based on the recommended heating system
high_heat_retention_contols_desc = "Controls for high heat retention storage heaters"
# We only recommend Celect-type controls if the current heating system is not Celect-type controls
if self.property.main_heating_controls["clean_description"] != high_heat_retention_contols_desc:
if self.property.main_heating_controls["clean_description"] != self.high_heat_retention_contols_desc:
controls_recommender.recommend(heating_description="Electric storage heaters, radiators")
# Conditions for not needing this recommendation
already_installed_hh_retention = (
"Electric storage heaters" in self.property.main_heating["clean_description"] and
self.property.main_heating_controls["clean_description"].lower() == high_heat_retention_contols_desc.lower()
)
has_hhr = self.is_hhr_already_installed()
# Conditions for not recommending electric storage heaters
if already_installed_hh_retention:
if has_hhr:
# No recommendation needed
return

279
recommendations/Mds.py
View file

@ -1,3 +1,5 @@
import itertools
from utils.logger import setup_logger
from backend.Property import Property
from recommendations.FloorRecommendations import FloorRecommendations
from recommendations.WallRecommendations import WallRecommendations
@ -12,13 +14,25 @@ from recommendations.HotwaterRecommendations import HotwaterRecommendations
from recommendations.SecondaryHeating import SecondaryHeating
from recommendations.Recommendations import Recommendations
logger = setup_logger()
class Mds:
"""
Handles the contruction of the MDS report
"""
def __init__(self, property_instance: Property, materials):
format_map = {
"external_wall_insulation": "EWI (Trad Const)",
"internal_wall_insualtion": "IWI",
"cavity_wall_insulation": "CWI",
"loft_insulation": "LI",
"air_source_heat_pump": "ASHP Htg",
"high_heat_retention_storage_heaters": "High Heat Retention Storage Heaters",
"solar_pv": "Solar PV",
}
def __init__(self, property_instance: Property, materials, optimise_measures: bool = False):
self.property_instance = property_instance
self.floor_recommender = FloorRecommendations(property_instance=property_instance, materials=materials)
@ -35,14 +49,169 @@ class Mds:
self.hotwater_recommender = HotwaterRecommendations(property_instance=property_instance)
self.secondary_heating_recommender = SecondaryHeating(property_instance=property_instance)
def build(self):
if self.property_instance.measures is None:
raise NotImplementedError("No measures in the property - implement me")
# This flag indicates that we wish to optimise the measures, to the property, depending on the set of measures
# we have been provided
self.optimise_measures = optimise_measures
measures = self.property_instance.measures
def select_optimal_measure_set(self, measures):
measure_config_list = [list(m.keys())[0] for m in measures]
# This is the set
all_considered_measures = [
'external_wall_insulation',
'cavity_wall_insulation',
'loft_insulation',
'air_source_heat_pump',
'high_heat_retention_storage_heaters',
'solar_pv'
]
# Check if our measures are within the ones we've handled
new = [m for m in measures if m not in all_considered_measures]
if new:
raise NotImplementedError("New measures - handle me")
def prune_options(options, measures):
options_pruned = []
for _group in options:
group_pruned = [m for m in _group if m in measures]
if not group_pruned:
continue
options_pruned.append(group_pruned)
return options_pruned
# For options in here, a property could only possibly have one of these
one_choice_options = [
["external_wall_insulation", "cavity_wall_insulation", "internal_wall_insulation"],
["loft_insulation", "flat_roof_insulation", "room_in_roof_insulation"],
["solid_floor_insulation", "suspended_floor_insulation"],
]
# prune one_choice_options based on the measure set considered for this property
one_choice_options_pruned = prune_options(one_choice_options, measures)
# For options in here, a property could have one or the other so all should be considered
multi_path_options = [
["air_source_heat_pump", "high_heat_retention_storage_heaters", "gas_boiler"]
]
multi_path_options_pruned = prune_options(multi_path_options, measures)
one_choice_combinations = [list(itertools.product(*one_choice_options_pruned))]
one_choice_combinations = [list(x) for sublist in one_choice_combinations for x in sublist]
multi_path_combinations = [list(itertools.product(*multi_path_options_pruned))]
multi_path_combinations = [list(x) for sublist in multi_path_combinations for x in sublist]
one_choice_flat = [item for sublist in one_choice_options_pruned for item in sublist]
multi_path_flat = [item for sublist in multi_path_options_pruned for item in sublist]
remaining_measures = [
measure for measure in measures
if measure not in one_choice_flat and measure not in multi_path_flat
]
# Combine one_choice and multi_path combinations with remaining measures
final_combinations = []
for one_choice in one_choice_combinations:
for multi_path in multi_path_combinations:
final_combinations.append([m for m in one_choice + multi_path + remaining_measures])
pruned_combinations = []
# TODO: We can do these checks once, outside of the loop and prune the combinations
for combination in final_combinations:
pruned_measures = []
for measure in combination:
if measure not in measures:
continue
# There are certain measures where we need to
if measure == "external_wall_insulation":
# Check if the wall is not cavity since the other wall types can take external wall insulation
if (
self.wall_recommender.ewi_valid() and
not self.property_instance.walls["insulation_thickness"] in ["average", "above average"]
):
pruned_measures.append(measure)
continue
if measure == "cavity_wall_insulation":
# Check if the wall is cavity
if (
self.property_instance.walls['is_cavity_wall'] and
not self.property_instance.walls['is_filled_cavity']
):
pruned_measures.append(measure)
continue
if measure == "loft_insulation":
# Check if the roof is suitable for loft insulation and the loft isn't already done
# Or, if the home had a u-value for the roof, we don't recommend loft insulation
if (
self.property_instance.roof["is_pitched"] and
not self.roof_recommender.is_loft_already_insulated() and
self.property_instance.roof["thermal_transmittance_unit"] is None
):
pruned_measures.append(measure)
continue
if measure == "solid_floor_insulation":
# Check if the floor is solid
if (
self.property_instance.floor["is_solid"] and
self.property_instance.floor["insulation_thickness"] not in ["average", "above average"] and
self.property_instance.floor["thermal_transmittance_unit"] is not None
):
pruned_measures.append(measure)
continue
if measure == "suspended_floor_insulation":
# Check if the floor is suspended
if (
self.property_instance.floor["is_suspended"] and
self.property_instance.floor["insulation_thickness"] not in ["average", "above average"] and
self.property_instance.floor["thermal_transmittance_unit"] is not None
):
pruned_measures.append(measure)
continue
if measure == "high_heat_retention_storage_heaters":
# For the moment, we recommend storage heaters if the property doesn't already
# and don't make it contngent on controls
already_has_hhr = self.heating_recommender.is_hhr_already_installed()
if (
self.heating_recommender.is_high_heat_retention_valid() and
not already_has_hhr
):
pruned_measures.append(measure)
continue
if measure == "air_source_heat_pump":
if self.heating_recommender.is_ashp_valid():
pruned_measures.append(measure)
continue
if measure == "solar_pv":
if self.solar_recommender.is_solar_pv_valid():
pruned_measures.append(measure)
continue
raise NotImplementedError("Implement me")
if not pruned_measures:
continue
pruned_measures_formatted = []
for pm in pruned_measures:
pruned_measures_formatted.append({pm: self.format_map[pm]})
pruned_combinations.append(pruned_measures_formatted)
# We're left with the subset of measures that are possible for this property
# These are the possible groups of measures that could be applied to this home
return pruned_combinations
def _build(self, measure_config_list, measures):
not_implemented_measures = [
"party_wall_insulation",
"ground_source_heat_pump",
@ -60,114 +229,164 @@ class Mds:
mds_recommendations = []
errors = []
phase = 0
# TODO: Could use a decarator to reduce the boilerplate code - insert_recommendation_id and then the append
if "external_wall_insulation" in measure_config_list:
recs = self.wall_recommender.mds_recommend_ewi(phase=0)
recs = self.wall_recommender.mds_recommend_ewi(phase=phase)
if not recs:
raise Exception("No recommendations for external wall insulation")
recs = self.insert_recommendation_id(recs, measures, "external_wall_insulation")
mds_recommendations.append(recs)
if self.optimise_measures and len(recs):
phase += 1
if "cavity_wall_insulation" in measure_config_list:
recs = self.wall_recommender.mds_recommend_cavity_wall_insulation(phase=0)
recs = self.wall_recommender.mds_recommend_cavity_wall_insulation(phase=phase)
recs = self.insert_recommendation_id(recs, measures, "cavity_wall_insulation")
mds_recommendations.append(recs)
if self.optimise_measures and len(recs):
phase += 1
if "loft_insulation" in measure_config_list:
# Check if the roof is suitable for loft insulation
if self.property_instance.roof['is_roof_room']:
errors.append("Roof is a room")
else:
recs = self.roof_recommender.mds_loft_insulation(phase=0)
recs = self.roof_recommender.mds_loft_insulation(phase=phase)
if not recs:
raise Exception("No recommendations for loft insulation")
recs = self.insert_recommendation_id(recs, measures, "loft_insulation")
mds_recommendations.append(recs)
if self.optimise_measures and len(recs):
phase += 1
if "internal_wall_insulation" in measure_config_list:
raise Exception("check me out 4")
self.wall_recommender.recommend(phase=0)
self.wall_recommender.recommend(phase=phase)
if "suspended_floor_insulation" in measure_config_list:
raise Exception("check me out 5")
self.floor_recommender.recommend(phase=0)
self.floor_recommender.recommend(phase=phase)
if "solid_floor_insulation" in measure_config_list:
raise Exception("check me out 6")
self.floor_recommender.recommend(phase=0)
self.floor_recommender.recommend(phase=phase)
if "air_source_heat_pump" in measure_config_list:
recs = self.heating_recommender.recommend_air_source_heat_pump(
phase=0, has_cavity_or_loft_recommendations=False, _return=True
phase=phase, has_cavity_or_loft_recommendations=False, _return=True
)
recs = self.insert_recommendation_id(recs, measures, "air_source_heat_pump")
mds_recommendations.append(recs)
if self.optimise_measures and len(recs):
phase += 1
if "electric_storage_heaters" in measure_config_list:
if "high_heat_retention_storage_heaters" in measure_config_list:
recs = self.heating_recommender.recommend_hhr_storage_heaters(
phase=0, system_change=True, heating_controls_only=False, _return=True
phase=phase, system_change=True, heating_controls_only=False, _return=True
)
recs = self.insert_recommendation_id(recs, measures, "electric_storage_heaters")
mds_recommendations.append(recs)
if recs is None:
logger.info(
f"No recommendations for high heat retention storage heaters, current heating "
f"{self.property_instance.main_heating['clean_description']}"
)
else:
recs = self.insert_recommendation_id(recs, measures, "high_heat_retention_storage_heaters")
mds_recommendations.append(recs)
if self.optimise_measures and len(recs):
phase += 1
if "low_energy_lighting" in measure_config_list:
raise Exception("check me out 9")
self.lighting_recommender.recommend(phase=0)
self.lighting_recommender.recommend(phase=phase)
if "cylinder_insulation" in measure_config_list:
raise Exception("check me out 10")
self.hotwater_recommender.recommend(phase=0)
self.hotwater_recommender.recommend(phase=phase)
if "smart_controls" in measure_config_list:
raise Exception("check me out 11")
self.heating_recommender.recommend(phase=0)
self.heating_recommender.recommend(phase=phase)
if "zone_controls" in measure_config_list:
raise Exception("check me out 12")
self.heating_recommender.recommend(phase=0)
self.heating_recommender.recommend(phase=phase)
if "trvs" in measure_config_list:
raise Exception("check me out 13")
self.heating_recommender.recommend(phase=0)
self.heating_recommender.recommend(phase=phase)
if "solar_pv" in measure_config_list:
recs = self.solar_recommender.mds_recommend(phase=0, solar_pv_percentage=0.5)
recs = self.solar_recommender.mds_recommend(phase=phase, solar_pv_percentage=0.5)
recs = self.insert_recommendation_id(recs, measures, "solar_pv")
mds_recommendations.append(recs)
if self.optimise_measures and len(recs):
phase += 1
if "double_glazing" in measure_config_list:
raise Exception("check me out 15")
self.windows_recommender.recommend(phase=0)
self.windows_recommender.recommend(phase=phase)
if "mechanical_ventilation" in measure_config_list:
raise Exception("check me out 16")
self.ventilation_recomender.recommend(phase=0)
self.ventilation_recomender.recommend(phase=phase)
if "gas_boiler" in measure_config_list:
raise Exception("check me out 17")
self.heating_recommender.recommend(phase=0)
self.heating_recommender.recommend(phase=phase)
if "flat_roof_insulation" in measure_config_list:
raise Exception("check me out 18")
self.roof_recommender.recommend(phase=0)
self.roof_recommender.recommend(phase=phase)
if "room_in_roof_insulation" in measure_config_list:
raise Exception("check me out 19")
self.roof_recommender.recommend(phase=0)
self.roof_recommender.recommend(phase=phase)
property_representative_recommendations = Recommendations.create_representative_recommendations(
mds_recommendations, non_invasive_recommendations=[]
)
return property_representative_recommendations, errors
return mds_recommendations, property_representative_recommendations, errors
def build(self):
if self.property_instance.measures is None:
raise NotImplementedError("No measures in the property - implement me")
if self.optimise_measures:
measures_set = self.select_optimal_measure_set(self.property_instance.measures)
mds_recommendations_map = {}
representative_recommendations_map = {}
errors_map = {}
for measures in measures_set:
measure_config_list = [list(x.keys())[0] for x in measures]
mds_recommendations, rep_recommendations, errors = self._build(
measure_config_list=measure_config_list,
measures=measures
)
if errors:
logger.info(f"Errors: {errors}")
mds_recommendations_map[str(measure_config_list)] = mds_recommendations
representative_recommendations_map[str(measure_config_list)] = rep_recommendations
errors_map[str(measure_config_list)] = errors
return mds_recommendations_map, representative_recommendations_map, errors_map
else:
measure_config_list = [list(m.keys())[0] for m in self.property_instance.measures]
return self._build(measure_config_list=measure_config_list, measures=self.property_instance.measures)
@staticmethod
def insert_recommendation_id(recommendations, measures, measure_name):
# Insert the recommendation identifier into this recommendation
measure_config = [m for m in measures if measure_name in m][0]
idx = 0
for r in recommendations:
r["recommendation_id"] = list(measure_config.values())[0]
r["recommendation_id"] = list(measure_config.values())[0] + "-" + str(idx)
idx += 1
return recommendations

15
recommendations/Recommendations.py
View file

@ -227,7 +227,7 @@ class Recommendations:
recommendations_by_type = sorted(recommendations_by_type, key=lambda x: x["type"])
representative_recommendations = []
for type, recommendations in groupby(recommendations_by_type, key=lambda x: x["type"]):
for _type, recommendations in groupby(recommendations_by_type, key=lambda x: x["type"]):
recommendations = list(recommendations)
# We also create an efficiency key, which is used to sort the recommendations
if has_u_value:
@ -311,14 +311,6 @@ class Recommendations:
# This is the unadjusted resulting heat demand
predicted_heat_demand_change = starting_heat_demand - expected_heat_demand
# We don't want to adjust the heat demand for mechanical ventilation so we add it back on
# We adjust the heat demand figures to align to the UCL paper
current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
epc_energy_consumption=starting_heat_demand,
current_epc_rating=property_instance.data["current-energy-rating"],
)
# TODO: This isn't quite right as this is based on EVERY possible measure, not just the ones that are
# actually implemented
expected_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
@ -327,11 +319,10 @@ class Recommendations:
)
adjusted_heat_demand_change = (
current_adjusted_energy - expected_adjusted_energy
property_instance.current_adjusted_energy - expected_adjusted_energy
)
# TODO: We should determine if the home is gas & electricity or just electricity
current_energy_bill = AnnualBillSavings.calculate_annual_bill(current_adjusted_energy)
expected_energy_bill = AnnualBillSavings.calculate_annual_bill(expected_adjusted_energy)
for recommendations_by_type in property_recommendations:
@ -410,8 +401,6 @@ class Recommendations:
return (
property_recommendations,
current_adjusted_energy,
expected_adjusted_energy,
current_energy_bill,
expected_energy_bill
)

33
recommendations/RoofRecommendations.py
View file

@ -54,6 +54,13 @@ class RoofRecommendations:
]
]
# Extract the insulation thickness from the roof, which is used throughout this method
self.insulation_thickness = convert_thickness_to_numeric(
self.property.roof["insulation_thickness"],
self.property.roof["is_pitched"],
self.property.roof["is_flat"]
)
def mds_loft_insulation(self, phase):
"""
For usages within the mds report
@ -62,18 +69,18 @@ class RoofRecommendations:
"""
self.recommendations = []
insulation_thickness = convert_thickness_to_numeric(
self.property.roof["insulation_thickness"],
self.property.roof["is_pitched"],
self.property.roof["is_flat"]
)
u_value = get_roof_u_value(**{**self.property.roof, "age_band": self.property.age_band})
self.recommend_roof_insulation(u_value, insulation_thickness, self.property.roof, phase)
self.recommend_roof_insulation(u_value, self.insulation_thickness, self.property.roof, phase)
return self.recommendations
def is_loft_already_insulated(self):
"""
Check if the loft is already insulated
"""
return (self.insulation_thickness > self.MINIMUM_LOFT_ISULATION_MM) and self.property.roof["is_pitched"]
def recommend(self, phase):
if self.property.roof["has_dwelling_above"]:
@ -81,21 +88,15 @@ class RoofRecommendations:
u_value = self.property.roof["thermal_transmittance"]
insulation_thickness = convert_thickness_to_numeric(
self.property.roof["insulation_thickness"],
self.property.roof["is_pitched"],
self.property.roof["is_flat"]
)
# We check if the roof is already insulated and if so, we exit
# Building regulations part L recommend installing at least 270mm of insulation, however generally we
# experience diminishing returns in terms of SAP once we go beyond around 150mm of insulation
# This only holds true for pitched roofs.
if (insulation_thickness > self.MINIMUM_LOFT_ISULATION_MM) and self.property.roof["is_pitched"]:
if self.is_loft_already_insulated():
return
if (insulation_thickness >= self.MINIMUM_FLAT_ROOF_ISULATION_MM) and self.property.roof["is_flat"]:
if (self.insulation_thickness >= self.MINIMUM_FLAT_ROOF_ISULATION_MM) and self.property.roof["is_flat"]:
return
if self.property.roof["is_roof_room"]:
@ -119,7 +120,7 @@ class RoofRecommendations:
return
if self.property.roof["is_pitched"] or self.property.roof["is_flat"]:
self.recommend_roof_insulation(u_value, insulation_thickness, self.property.roof, phase)
self.recommend_roof_insulation(u_value, self.insulation_thickness, self.property.roof, phase)
return
if self.property.roof["is_roof_room"]:

30
recommendations/SolarPvRecommendations.py
View file

@ -4,10 +4,13 @@ from recommendations.recommendation_utils import override_costs
class SolarPvRecommendations:
# Solar panel specs based on Eurener 400s solar panels
# https://midsummerwholesale.co.uk/buy/eurener/eurener-400w-mepv-zebra-ab-half-cut-mono
# Approximate area of the solar panels
SOLAR_PANEL_AREA = 1.6
SOLAR_PANEL_AREA = 1.79
# Wattage per panel - this is based on the average wattage of a solar panel being between 250w and 420w
SOLAR_PANEL_WATTAGE = 250
# This was previously set to 250w, but has been upped to 400 based on the systems used by Cotswolrd Energy Group
SOLAR_PANEL_WATTAGE = 400
MAX_SYSTEM_WATTAGE = 6000
MIN_SYSTEM_WATTAGE = 1000
@ -75,15 +78,7 @@ class SolarPvRecommendations:
}
]
def recommend(self, phase):
"""
We check if a property is potentially suitable for solar PV based on the following criteria:
- The property is a house or bungalow
- The property has a flat or pitched roof
- The property does not have existing solar pv
:return:
"""
def is_solar_pv_valid(self):
is_valid_property_type = self.property.data["property-type"] in ["House", "Bungalow", "Maisonette"]
is_valid_roof_type = (
self.property.roof["is_flat"] or self.property.roof["is_pitched"] or self.property.roof["is_roof_room"]
@ -93,7 +88,18 @@ class SolarPvRecommendations:
None, 0, self.property.DATA_ANOMALY_MATCHES
]
if not is_valid_property_type or not is_valid_roof_type or not has_no_existing_solar_pv:
return is_valid_property_type and is_valid_roof_type and has_no_existing_solar_pv
def recommend(self, phase):
"""
We check if a property is potentially suitable for solar PV based on the following criteria:
- The property is a house or bungalow
- The property has a flat or pitched roof
- The property does not have existing solar pv
:return:
"""
if not self.is_solar_pv_valid():
return
solar_pv_percentage = self.property.solar_pv_percentage

120
recommendations/WallRecommendations.py
View file

@ -55,23 +55,26 @@ class WallRecommendations(Definitions):
NEW_BUILD_INSULATED = 0.75
# These are the ending descriptions we consider for walls with external insulation
# This maps the clean descriptions to the ending descriptions
EXTERNALLY_INSULATED_WALL_DESCRIPTIONS = {
"solid_brick": "Solid brick, with external insulation",
"cob": "Cob, with external insulation",
"system_built": "System built, with external insulation",
"granite_or_whinstone": 'Granite or whinstone, with external insulation',
"sandstone_or_limestone": 'Sandstone or limestone, with external insulation',
"timber_frame": "Timber frame, with external insulation"
"Cavity wall, as built, insulated": "Cavity wall, filled cavity and external insulation",
"Solid brick, as built, no insulation": "Solid brick, with external insulation",
"Solid brick, as built, insulated": "Solid brick, with external insulation",
"Cob, as built": "Cob, with external insulation",
"System built, as built, no insulation": "System built, with external insulation",
"Granite or whinstone, as built, no insulation": 'Granite or whinstone, with external insulation',
"Timber frame, as built, no insulation": "Timber frame, with external insulation",
}
# These are the ending descriptions we consider for walls with internal insulation
INTERNALLY_INSULATED_WALL_DESCRIPTIONS = {
"solid_brick": "Solid brick, with internal insulation",
"cob": "Cob, with internal insulation",
"system_built": "System built, with internal insulation",
"granite_or_whinstone": 'Granite or whinstone, with internal insulation',
"sandstone_or_limestone": 'Sandstone or limestone, with internal insulation',
"timber_frame": "Timber frame, with internal insulation"
"Cavity wall, as built, insulated": "Cavity wall, filled cavity and internal insulation",
"Solid brick, as built, no insulation": "Solid brick, with internal insulation",
"Solid brick, as built, insulated": "Solid brick, with internal insulation",
"Cob, as built": "Cob, with internal insulation",
"System built, as built, no insulation": "System built, with internal insulation",
"Granite or whinstone, as built, no insulation": 'Granite or whinstone, with internal insulation',
"Timber frame, as built, no insulation": "Timber frame, with internal insulation",
}
def __init__(
@ -99,7 +102,7 @@ class WallRecommendations(Definitions):
part
for part in materials
if part["type"]
in ["iwi_wall_demolition", "iwi_vapour_barrier", "iwi_redecoration"]
in ["iwi_wall_demolition", "iwi_vapour_barrier", "iwi_redecoration"]
]
self.external_wall_insulation_materials = [
@ -109,11 +112,9 @@ class WallRecommendations(Definitions):
self.external_wall_non_insulation_materials = [
part
for part in materials
if part["type"]
in ["ewi_wall_demolition", "ewi_wall_preparation", "ewi_wall_redecoration"]
if part["type"] in ["ewi_wall_demolition", "ewi_wall_preparation", "ewi_wall_redecoration"]
]
@property
def ewi_valid(self):
"""
This method check available data, to determine if a property is suitable for external wall insulation
@ -123,11 +124,24 @@ class WallRecommendations(Definitions):
# it is not suitable for EWI
if self.property.restricted_measures or (
self.property.data["property-type"].lower() == "flat"
) or (
self.property.walls['is_cob'] or
self.property.walls['is_sandstone_or_limestone'] or
self.property.walls["is_cavity_wall"]
):
return False
return True
def is_suitable_for_solid_insulation(self):
"""
Checks if the wall is of a suitable type for internal/external wall insulation
"""
if self.property.walls["is_cavity_wall"] or self.property.walls["is_cob"]:
return False
return True
def mds_recommend_cavity_wall_insulation(self, phase=None):
# Function specifically for cavity wall insulation, for usage in the mds report
self.recommendations = []
@ -175,7 +189,7 @@ class WallRecommendations(Definitions):
# recommend internal wall insulation as a possible measure
u_value = self.property.walls["thermal_transmittance"]
u_value = None if math.isnan(u_value) else u_value
u_value = None if pd.isnull(u_value) else u_value
is_cavity_wall = self.property.walls["is_cavity_wall"]
insulation_thickness = self.property.walls["insulation_thickness"]
@ -246,7 +260,7 @@ class WallRecommendations(Definitions):
return
# Remaining wall types are treated with IWI or EWI
if u_value >= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE:
if (u_value >= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE) and self.is_suitable_for_solid_insulation():
self.find_insulation(u_value, phase)
return
@ -332,18 +346,19 @@ class WallRecommendations(Definitions):
wall_ending_config = WallAttributes("Cavity wall, filled cavity").process()
simulation_config = {}
if self.property.data["walls-energy-eff"] not in ["Good", "Very Good"]:
simulation_config = {
"walls_energy_eff_ending": "Good",
"walls_thermal_transmittance_ending": new_u_value
}
walls_simulation_config = check_simulation_difference(
new_config=wall_ending_config, old_config=self.property.walls, prefix="walls_"
)
simulation_config = {**simulation_config, **walls_simulation_config}
simulation_config = self.set_starting_simulation_config(
wall_ending_config=wall_ending_config
)
simulation_config = {
**simulation_config,
**walls_simulation_config,
"walls_thermal_transmittance_ending": new_u_value
}
recommendations.append(
{
@ -370,30 +385,35 @@ class WallRecommendations(Definitions):
self.recommendations = recommendations
def get_internal_external_wall_description(self, description_map, new_u_value):
if self.property.walls["is_solid_brick"]:
return description_map["solid_brick"]
if self.property.walls["is_cob"]:
return description_map["cob"]
if self.property.walls["is_system_built"]:
return description_map["system_built"]
if self.property.walls["is_granite_or_whinstone"]:
return description_map["granite_or_whinstone"]
if self.property.walls["is_sandstone_or_limestone"]:
return description_map["sandstone_or_limestone"]
if self.property.walls["is_timber_frame"]:
return description_map["timber_frame"]
if "Average thermal transmittance" in self.property.walls["clean_description"]:
if new_u_value is None:
raise ValueError("New u value is None")
return f'Average thermal transmittance {new_u_value} W/m-¦K'
raise NotImplementedError("Not implemented yet")
return description_map[self.property.walls["clean_description"]]
def set_starting_simulation_config(self, wall_ending_config):
"""
Helper function to set the starting simulation config
"""
simulation_config = {}
if self.property.data["walls-energy-eff"] not in ["Good", "Very Good"]:
simulation_config = {
"walls_energy_eff_ending": "Good"
}
# We check if we have double insulation in any instances
double_insulation = (
(wall_ending_config["is_filled_cavity"] and wall_ending_config["external_insulation"]) or
(wall_ending_config["is_filled_cavity"] and wall_ending_config["internal_insulation"]) or
(wall_ending_config["external_insulation"] and wall_ending_config["internal_insulation"])
)
if double_insulation:
simulation_config["walls_energy_eff_ending"] = "Very Good"
return simulation_config
def _find_insulation(self, u_value, insulation_materials, non_insulation_materials, phase):
@ -468,16 +488,14 @@ class WallRecommendations(Definitions):
wall_ending_config = WallAttributes(new_description).process()
simulation_config = {}
if self.property.data["walls-energy-eff"] not in ["Good", "Very Good"]:
simulation_config = {
"walls_energy_eff_ending": "Good"
}
walls_simulation_config = check_simulation_difference(
new_config=wall_ending_config, old_config=self.property.walls, prefix="walls_"
)
simulation_config = self.set_starting_simulation_config(
wall_ending_config=wall_ending_config
)
simulation_config = {
**walls_simulation_config,
**simulation_config,
@ -521,7 +539,7 @@ class WallRecommendations(Definitions):
# consider diminishing returns between the two as they are considered to be separate measures
ewi_recommendations = []
if self.ewi_valid:
if self.ewi_valid():
ewi_recommendations = self._find_insulation(
u_value=u_value,
insulation_materials=pd.DataFrame(

10
recommendations/recommendation_utils.py
View file

@ -756,17 +756,23 @@ def calculate_cavity_age(newest_epc, older_epcs, cleaned):
return cavity_age
def check_simulation_difference(old_config, new_config, prefix=""):
def check_simulation_difference(old_config, new_config, prefix="", keys_with_prefix=None):
"""
Given two dictionaries, that describe the heating control configurations, this method will compare the two
and pick out the differences. These differences will be things that have been added and things that have been
removed. This will be used to determine how we should be updating the configuration in the simulation
:return:
"""
keys_with_prefix = (
["is_assumed", "thermal_transmittance", "insulation_thickness"] if keys_with_prefix is None
else keys_with_prefix
)
differences = {}
for key in new_config:
if old_config[key] != new_config[key]:
new_key = prefix + key + "_ending" if key in ["is_assumed", "thermal_transmittance"] else key + "_ending"
new_key = prefix + key + "_ending" if key in keys_with_prefix else key + "_ending"
differences[new_key] = new_config[key]
return differences