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handling the odd case of a double property

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Khalim Conn-Kowlessar 2024-08-01 22:07:19 +01:00
parent 1aee76dac1
commit e43842d980
9 changed files with 307 additions and 10 deletions
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123
backend/apis/GoogleSolarApi.py
View file

@ -9,6 +9,7 @@ from backend.app.db.functions.solar_functions import get_solar_data, store_batch
from utils.logger import setup_logger
from sklearn.preprocessing import MinMaxScaler
from recommendations.Costs import Costs
from math import sin, cos, sqrt, atan2, radians
logger = setup_logger()
@ -70,6 +71,9 @@ class GoogleSolarApi:
# Indicates if we need to store the data to the db
self.need_to_store = False
# Indicates if we think we have both units attached to a semi-detached property
self.double_property = False
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
@ -116,7 +120,7 @@ class GoogleSolarApi:
required_quality="MEDIUM",
is_building=False,
session=None,
uprn=None
uprn=None,
):
"""
Wrapper function that calls get_building_insights and extracts roof segments, with caching.
@ -147,6 +151,12 @@ class GoogleSolarApi:
# Extract key data from the insights response
self.roof_segments = self.insights_data["solarPotential"].get('roofSegmentStats', [])
# Automatically exclude north-facing segments
self.exclude_north_facing_segments()
# If a property is semi-detached, it's possible for us to include segments from an attached unit
if property_instance.data["built-form"] == "Semi-Detached":
self.exclude_likely_duplicate_surfaces()
self.roof_area = self.insights_data["solarPotential"]["wholeRoofStats"]['areaMeters2']
self.floor_area = self.insights_data["solarPotential"]["wholeRoofStats"]['groundAreaMeters2']
self.panel_area = (
@ -162,9 +172,6 @@ class GoogleSolarApi:
# 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
@ -172,6 +179,11 @@ class GoogleSolarApi:
energy_consumption=energy_consumption, is_building=is_building, property_instance=property_instance
)
# Finally, if we have a double property, we half the data we stored area
if self.double_property:
self.roof_area = self.roof_area / 2
self.floor_area = self.floor_area / 2
def save_to_db(self, session, uprns_to_location, scenario_type):
if self.insights_data is None:
raise ValueError("No api data to store")
@ -338,7 +350,13 @@ class GoogleSolarApi:
# - surplus: this is the amount of additional energy generated, and therefore how much will be exported
# - surplus_value: the value of the surplus energy - this feeds into generation_value, when relevant
# - expected_payback_years: the number of years it will take to pay back the initial investment
lifetime_energy_consumption = energy_consumption * self.installation_life_span
# If we have a double property (i.e. the solar api has returned data for two units) we size up the solar panels
# for double the consumption, as if for two units.
if self.double_property:
lifetime_energy_consumption = energy_consumption * 2 * self.installation_life_span
else:
lifetime_energy_consumption = energy_consumption * self.installation_life_span
roi_results = []
for _, panel_config in panel_performance.iterrows():
lifetime_ac_kwh = panel_config["lifetime_ac_kwh"]
@ -408,6 +426,31 @@ class GoogleSolarApi:
panel_performance["expected_payback_years"] = np.ceil(panel_performance["expected_payback_years"]).astype(int)
if self.double_property:
# Now that we've optimise to an energy consumption that is double the original, we need to half the
# results
panel_performance["n_panels_halved"] = panel_performance["n_panels"] / 2
n_panels_required = {int(x) for x in np.floor(panel_performance["n_panels"] / 2)}
# We filter the data on this number of panels
panel_performance = panel_performance[panel_performance["n_panels_halved"].isin(n_panels_required)]
# We half the generation values
for col in [
"yearly_dc_energy",
"total_cost",
"panneled_roof_area",
"array_wattage",
"initial_ac_kwh_per_year",
"lifetime_ac_kwh",
"lifetime_dc_kwh",
"generation_value",
"generation_deficit",
"surplus"
]:
panel_performance[col] = panel_performance[col] / 2
panel_performance["n_panels"] = panel_performance["n_panels_halved"]
panel_performance = panel_performance.drop(columns=["n_panels_halved"])
self.panel_performance = panel_performance
def exclude_north_facing_segments(self):
@ -427,3 +470,73 @@ class GoogleSolarApi:
filtered_segments.append(segment)
self.roof_segments = filtered_segments
@staticmethod
def haversine(lat1, lon1, lat2, lon2):
"""
Calculate the great-circle distance between two points on the Earth
given their latitude and longitude in decimal degrees. Using haversine formula.
"""
R = 6373.0 # approximate radius of earth in km
lat1 = radians(lat1)
lon1 = radians(lon1)
lat2 = radians(lat2)
lon2 = radians(lon2)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance = R * c
return distance
def exclude_likely_duplicate_surfaces(self):
"""
By checking the azimuth of the segments, we can exclude any segments that are likely to be duplicates
:return:
"""
def is_similar(segment1, segment2, azimuth_tol=20):
azimuth_diff = abs(segment1['azimuthDegrees'] - segment2['azimuthDegrees'])
return azimuth_diff <= azimuth_tol
property_center = self.insights_data["center"]
deduped_segments = []
for segment in self.roof_segments:
if not deduped_segments:
deduped_segments.append(segment)
continue
similar_segments = [s for s in deduped_segments if is_similar(segment, s)]
if not similar_segments:
deduped_segments.append(segment)
else:
# Compare distances to the property center and keep the closer segment
for similar_segment in similar_segments:
current_dist = self.haversine(
property_center['latitude'], property_center['longitude'],
segment['center']['latitude'], segment['center']['longitude']
)
similar_dist = self.haversine(
property_center['latitude'], property_center['longitude'],
similar_segment['center']['latitude'], similar_segment['center']['longitude']
)
if current_dist < similar_dist:
deduped_segments.remove(similar_segment)
deduped_segments.append(segment)
# If we have a semi-detached property that has duplicated segments, we should expect to half the number of
# segments
if len(deduped_segments) < len(self.roof_segments):
if len(deduped_segments) != len(self.roof_segments) / 2:
raise ValueError("We don't have half the number of segments that we started with")
# Because the segments are duplicated, but the sizes aren't necessarily split perfectly in half, what
# we need to do is perform the solar analysis and then half the results. We set an indicator which
# implies we should do this
self.double_property = True

13
backend/app/plan/router.py
View file

@ -439,6 +439,8 @@ async def trigger_plan(body: PlanTriggerRequest):
logger.info("Performing solar analysis")
# TODO: Tidy this up
# TODO: If a property is semi-detached, we might get roof surfaces for the main building + the neighbour
#
building_ids = [
{
"building_id": p.building_id,
@ -709,6 +711,17 @@ async def trigger_plan(body: PlanTriggerRequest):
]
recommendations[property_id] = final_recommendations
# df = []
# for rec in recommendations[list(recommendations.keys())[0]]:
# df.append(
# {
# "id": rec["recommendation_id"],
# "description": rec["description"],
# "sap": rec["sap_points"],
# }
# )
# df = pd.DataFrame(df)
# 1) the property data
# 2) the property details (epc)
# 3) the recommendations

2
etl/bill_savings/data_collection.py
View file

@ -131,7 +131,9 @@ def app():
sample_size = 500
energy_consumption_data = []
cavity_walls_data = []
for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
# Skip the first 50
# if i < 57:
# continue

90
etl/energy_efficiency/app.py Normal file
View file

@ -0,0 +1,90 @@
import inspect
import pandas as pd
from tqdm import tqdm
from pathlib import Path
src_file_path = inspect.getfile(lambda: None)
EPC_DIRECTORY = Path(src_file_path).parent / "local_data" / "all-domestic-certificates"
def app():
# For EPCs lodged from 2020 onwards, this collects data on the energy efficiency categories for wall insulation
# so that when we simulate, we know what the resulting energy efficiency category will be
epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
date_cutoff = "2020-01-01"
walls_data = []
ashp_data = []
for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
data = pd.read_csv(directory / "certificates.csv", low_memory=False)
# Rename the columns to the same format as the api returns
data.columns = [c.replace("_", "-").lower() for c in data.columns]
insulated_walls = data[
data["walls-description"].isin(
[
"Cavity wall, filled cavity",
"Solid brick, with internal insulation",
"Solid brick, with external insulation",
]
)
]
insulated_walls = insulated_walls[~pd.isnull(insulated_walls["uprn"])]
insulated_walls = insulated_walls[
pd.to_datetime(insulated_walls["lodgement-date"]) >= date_cutoff
]
ashp = data[
data["mainheat-description"] == "Air source heat pump, radiators, electric"
]
ashp = ashp[~pd.isnull(ashp["uprn"])]
ashp = ashp[
pd.to_datetime(ashp["lodgement-date"]) >= date_cutoff
]
walls_data.append(insulated_walls)
ashp_data.append(ashp)
walls_df = pd.concat(walls_data)
ashp_df = pd.concat(ashp_data)
ashp_agg = (
ashp_df.
groupby(
["construction-age-band", "mainheat-description", "mainheatcont-description", "mainheat-energy-eff",
"mainheatc-energy-eff"]
)
.size()
.reset_index()
)
ashp_agg = ashp_agg[
ashp_agg["mainheatcont-description"].isin(
["Programmer, TRVs and bypass", "Time and temperature zone control"]
)
]
aggregations = {}
for description in [
"Cavity wall, filled cavity", "Solid brick, with internal insulation", "Solid brick, with external insulation"
]:
aggregation = walls_df[
walls_df["walls-description"] == description
].groupby(
["construction-age-band", "walls-energy-eff"]
).size().reset_index().rename(columns={0: "count"})
# For each grouping of age band, we use the most populus energy efficiency category
aggregation_deduped = aggregation.sort_values("count", ascending=False).drop_duplicates("construction-age-band")
aggregations[description] = aggregation_deduped
# Since these tables are small, we just convert them to python dictionaries
# This data is just held in the wall_energy_efficiency_values script, rather than s3
df1 = aggregations["Cavity wall, filled cavity"]
df2 = aggregations["Solid brick, with internal insulation"]
df3 = aggregations["Solid brick, with external insulation"]
df1.to_dict("records")
df2.to_dict("records")
df3.to_dict("records")

6
etl/xml_survey_extraction/app.py
View file

@ -27,7 +27,7 @@ SCENARIOS = {
"already_installed_file_path": "",
"patches_file_path": "",
"non_invasive_recommendations_file_path": "",
"exclusions": ["floor_insulation", "fireplace", "solar_pv", "heating"],
"exclusions": ["floor_insulation", "fireplace", "solar_pv", "heating", 'lighting'],
"budget": None,
"scenario_name": "Low Hanging Fruit",
"multi_plan": True,
@ -42,7 +42,7 @@ SCENARIOS = {
"already_installed_file_path": "",
"patches_file_path": "",
"non_invasive_recommendations_file_path": "",
"exclusions": ["floor_insulation", "fireplace"],
"exclusions": ["floor_insulation", "fireplace", 'lighting'],
"budget": None,
"scenario_name": "Deep Retrofit",
"multi_plan": True,
@ -57,7 +57,7 @@ SCENARIOS = {
"already_installed_file_path": "",
"patches_file_path": "",
"non_invasive_recommendations_file_path": "",
"exclusions": ["fireplace"],
"exclusions": ["fireplace", 'lighting'],
"budget": None,
"scenario_name": "Whole House Retrofit",
"multi_plan": True,

6
recommendations/HeatingControlRecommender.py
View file

@ -291,7 +291,11 @@ class HeatingControlRecommender:
simulation_config = check_simulation_difference(
new_config=ending_config, old_config=self.property.main_heating_controls
)
simulation_config["mainheatc_energy_eff_ending"] = "Average"
# Only adjust if the current system is below good
if self.property.data["mainheatc-energy-eff"] in ["Poor", "Very Poor"]:
simulation_config["mainheatc_energy_eff_ending"] = "Average"
else:
simulation_config["mainheatc_energy_eff_ending"] = self.property.data["mainheatc-energy-eff"]
description_simulation = {
"mainheatcont-description": new_controls_description,

1
recommendations/HeatingRecommender.py
View file

@ -257,6 +257,7 @@ class HeatingRecommender:
f" The cost includes the £{BOILER_UPGRADE_SCHEME_ASHP_VALUE} boiler upgrade scheme grant"
)
print("TEMP UPDATED FOR 77 Perryn!!!!!")
simulation_config = {
"mainheat_energy_eff_ending": "Good",
"hot_water_energy_eff_ending": "Good"

20
recommendations/WallRecommendations.py
View file

@ -13,6 +13,7 @@ from recommendations.recommendation_utils import (
)
from recommendations.config import PARTIALLY_FILLED_PERCENTAGE_ASSUMPTION
from recommendations.Costs import Costs
from recommendations.wall_energy_efficiency_values import cavity_wall_energy_eff, iwi_energy_eff, ewi_energy_eff
from utils.logger import setup_logger
logger = setup_logger()
@ -404,11 +405,28 @@ class WallRecommendations(Definitions):
simulation_config = {}
if self.property.data["walls-energy-eff"] not in ["Good", "Very Good"]:
if wall_ending_config["is_cavity_wall"]:
efficiency_data = [
x for x in cavity_wall_energy_eff if
x["construction-age-band"] == self.property.construction_age_band
][0]
elif wall_ending_config["internal_insulation"]:
efficiency_data = [
x for x in iwi_energy_eff if
x["construction-age-band"] == self.property.construction_age_band
][0]
else:
efficiency_data = [
x for x in ewi_energy_eff if
x["construction-age-band"] == self.property.construction_age_band
][0]
simulation_config = {
"walls_energy_eff_ending": "Good"
"walls_energy_eff_ending": efficiency_data["walls-energy-eff"]
}
# We check if we have double insulation in any instances
# TODO: We should pull the energy efficiency categories on double insulation instances, though it's quite rate
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

56
recommendations/wall_energy_efficiency_values.py Normal file
View file

@ -0,0 +1,56 @@
cavity_wall_energy_eff = [
{'construction-age-band': 'England and Wales: 1950-1966', 'walls-energy-eff': 'Average', 'count': 605820},
{'construction-age-band': 'England and Wales: 1967-1975', 'walls-energy-eff': 'Average', 'count': 410998},
{'construction-age-band': 'England and Wales: 1930-1949', 'walls-energy-eff': 'Average', 'count': 263575},
{'construction-age-band': 'England and Wales: 1976-1982', 'walls-energy-eff': 'Good', 'count': 206654},
{'construction-age-band': 'England and Wales: 1983-1990', 'walls-energy-eff': 'Good', 'count': 106489},
{'construction-age-band': 'England and Wales: 1900-1929', 'walls-energy-eff': 'Average', 'count': 58399},
{'construction-age-band': 'England and Wales: 1991-1995', 'walls-energy-eff': 'Good', 'count': 58252},
{'construction-age-band': 'England and Wales: 1996-2002', 'walls-energy-eff': 'Good', 'count': 35141},
{'construction-age-band': 'England and Wales: 2003-2006', 'walls-energy-eff': 'Good', 'count': 7194},
{'construction-age-band': 'England and Wales: 2007-2011', 'walls-energy-eff': 'Good', 'count': 2639},
{'construction-age-band': 'England and Wales: before 1900', 'walls-energy-eff': 'Average', 'count': 2495},
{'construction-age-band': 'England and Wales: 2012 onwards', 'walls-energy-eff': 'Very Good', 'count': 1158},
{'construction-age-band': 'England and Wales: 2007 onwards', 'walls-energy-eff': 'Good', 'count': 357},
{'construction-age-band': 'INVALID!', 'walls-energy-eff': 'Very Good', 'count': 88}
]
iwi_energy_eff = [
{'construction-age-band': 'England and Wales: 1900-1929', 'walls-energy-eff': 'Good', 'count': 22415},
{'construction-age-band': 'England and Wales: before 1900', 'walls-energy-eff': 'Good',
'count': 13422},
{'construction-age-band': 'England and Wales: 1930-1949', 'walls-energy-eff': 'Good', 'count': 6640},
{'construction-age-band': 'England and Wales: 1950-1966', 'walls-energy-eff': 'Good', 'count': 1391},
{'construction-age-band': 'England and Wales: 1967-1975', 'walls-energy-eff': 'Good', 'count': 663},
{'construction-age-band': 'England and Wales: 2003-2006', 'walls-energy-eff': 'Very Good',
'count': 516},
{'construction-age-band': 'England and Wales: 2007-2011', 'walls-energy-eff': 'Very Good',
'count': 463},
{'construction-age-band': 'England and Wales: 2012 onwards', 'walls-energy-eff': 'Very Good',
'count': 353},
{'construction-age-band': 'England and Wales: 1996-2002', 'walls-energy-eff': 'Good', 'count': 218},
{'construction-age-band': 'England and Wales: 1983-1990', 'walls-energy-eff': 'Very Good',
'count': 166},
{'construction-age-band': 'England and Wales: 1976-1982', 'walls-energy-eff': 'Very Good',
'count': 121},
{'construction-age-band': 'England and Wales: 1991-1995', 'walls-energy-eff': 'Good', 'count': 104},
{'construction-age-band': 'England and Wales: 2007 onwards', 'walls-energy-eff': 'Very Good',
'count': 74}, {'construction-age-band': 'INVALID!', 'walls-energy-eff': 'Very Good', 'count': 26}
]
ewi_energy_eff = [
{'construction-age-band': 'England and Wales: 1900-1929', 'walls-energy-eff': 'Good', 'count': 18427},
{'construction-age-band': 'England and Wales: 1930-1949', 'walls-energy-eff': 'Good', 'count': 17803},
{'construction-age-band': 'England and Wales: 1950-1966', 'walls-energy-eff': 'Good', 'count': 4306},
{'construction-age-band': 'England and Wales: before 1900', 'walls-energy-eff': 'Good', 'count': 2955},
{'construction-age-band': 'England and Wales: 1967-1975', 'walls-energy-eff': 'Good', 'count': 647},
{'construction-age-band': 'England and Wales: 1976-1982', 'walls-energy-eff': 'Very Good', 'count': 188},
{'construction-age-band': 'England and Wales: 2007-2011', 'walls-energy-eff': 'Very Good', 'count': 73},
{'construction-age-band': 'England and Wales: 2003-2006', 'walls-energy-eff': 'Very Good', 'count': 49},
{'construction-age-band': 'England and Wales: 2012 onwards', 'walls-energy-eff': 'Very Good', 'count': 37},
{'construction-age-band': 'England and Wales: 1983-1990', 'walls-energy-eff': 'Good', 'count': 31},
{'construction-age-band': 'England and Wales: 1996-2002', 'walls-energy-eff': 'Very Good', 'count': 21},
{'construction-age-band': 'England and Wales: 1991-1995', 'walls-energy-eff': 'Good', 'count': 14},
{'construction-age-band': 'England and Wales: 2007 onwards', 'walls-energy-eff': 'Very Good', 'count': 8},
{'construction-age-band': 'INVALID!', 'walls-energy-eff': 'Very Good', 'count': 4}
]