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Merge pull request #295 from Hestia-Homes/immo-pilot

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Immo pilot
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KhalimCK 2024-04-23 15:34:55 +01:00 • committed by GitHub
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10 changed files with 243 additions and 56 deletions
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9
backend/SearchEpc.py
View file

@ -709,8 +709,13 @@ class SearchEpc:
self.full_sap_epc = {}
# Finally, set a standardised address 1 and postcode
self.address_clean = self.ordnance_survey_client.address_os
self.postcode_clean = self.ordnance_survey_client.postcode_os
self.address_clean = (
self.ordnance_survey_client.address_os if self.ordnance_survey_client.address_os else self.address1
)
self.postcode_clean = (
self.ordnance_survey_client.postcode_os if self.ordnance_survey_client.postcode_os else
self.postcode
)
return
os_response = self.ordnance_survey_client.get_places_api()

32
backend/app/plan/router.py
View file

@ -52,6 +52,10 @@ def patch_epc(patch, epc_records):
"""
for patch_variable, patch_value in patch.items():
if patch_variable in ["address", "postcode"]:
continue
if patch_value == "":
continue
if patch_variable in epc_records["original_epc"]:
@ -268,23 +272,26 @@ async def trigger_plan(body: PlanTriggerRequest):
postcode=config["postcode"],
uprn=uprn,
auth_token=get_settings().EPC_AUTH_TOKEN,
os_api_key=get_settings().ORDNANCE_SURVEY_API_KEY
os_api_key=get_settings().ORDNANCE_SURVEY_API_KEY,
)
epc_searcher.find_property()
epc_searcher.ordnance_survey_client.built_form = config.get("built_form", None)
epc_searcher.ordnance_survey_client.property_type = config.get("property_type", None)
# For the moment, our OS API access is unavailable, so we skip and interpolate
epc_searcher.find_property(skip_os=True)
# Create a record in db
property_id, is_new = create_property(
session, body.portfolio_id, epc_searcher.address_clean, epc_searcher.postcode_clean, epc_searcher.uprn
)
if not is_new:
continue
create_property_targets(
session,
property_id=property_id,
portfolio_id=body.portfolio_id,
epc_target=body.goal_value,
heat_demand_target=None
)
# if not is_new:
# continue
#
# create_property_targets(
# session,
# property_id=property_id,
# portfolio_id=body.portfolio_id,
# epc_target=body.goal_value,
# heat_demand_target=None
# )
epc_records = {
'original_epc': epc_searcher.newest_epc.copy(),
@ -373,6 +380,7 @@ async def trigger_plan(body: PlanTriggerRequest):
logger.info("Preparing data for scoring in sap change api")
recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
recommendations_scoring_data = recommendations_scoring_data.drop(
columns=["rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
"carbon_ending"]

8
backend/ml_models/Valuation.py
View file

@ -63,6 +63,14 @@ class PropertyValuation:
90093693: 279_000, # Based on Zoopla
90055152: 149_000, # Based on Zoopla
90028499: 238_000, # Based on Zoopla
# IMMO Dudley Pilot 2- search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
90039318: 177_000, # Based on Zoopla
90038384: 170_000, # Based on Zoopla
90105380: 185_000, # Based on Zoopla
90124001: 165_000, # Based on Zoopla
90013980: 148_000, # Based on Zoopla
90087154: 184_000, # Based on Zoopla
90046817: 167_000, # Based on Zoopla
}
# We base our valuation uplifts on a number of sources

5
etl/customers/gla_croydon_demo/asset_list.py
View file

@ -34,8 +34,9 @@ def app():
low_memory=False
)
z = epc_data.groupby(["WALLS_DESCRIPTION", "WALLS_ENERGY_EFF"]).size().reset_index(name="count")
z = z[z["MAINHEAT_DESCRIPTION"] == "Boiler and radiators, mains gas"]
z = epc_data[epc_data["MAINHEAT_DESCRIPTION"] == "Boiler and radiators, mains gas"]
z["HOTWATER_DESCRIPTION"].value_counts()
z["MAIN_FUEL"].value_counts()
# Filter on entries where we have a UPRN
epc_data = epc_data[~pd.isnull(epc_data["UPRN"])]

152
etl/customers/immo/pilot/asset_list_2.py Normal file
View file

@ -0,0 +1,152 @@
import pandas as pd
from utils.s3 import read_excel_from_s3
from utils.s3 import save_csv_to_s3
USER_ID = 8
PORTFOLIO_ID = 72
# For
patches = [
{
'address': '116 Parkes Hall Road',
'postcode': 'DY1 3RJ',
'uprn': '90046817',
'walls-description': 'Cavity wall, filled cavity',
'walls-energy-eff': 'Average',
'roof-description': 'Pitched, 270 mm loft insulation',
'roof-energy-eff': 'Good',
'windows-description': 'Fully double glazed',
'windows-energy-eff': 'Good',
'mainheat-description': 'Boiler and radiators, mains gas',
'mainheat-energy-eff': 'Good',
'mainheatcont-description': 'Programmer, room thermostat and TRVs',
'mainheatc-energy-eff': 'Good',
'lighting-description': 'Low energy lighting in 27% of fixed outlets',
'lighting-energy-eff': 'Average',
'floor-description': 'Solid, no insulation (assumed)',
'secondheat-description': 'None',
'current-energy-efficiency': '73',
'current-energy-rating': 'C',
'energy-consumption-current': '184',
'co2-emissions-current': '2.4',
'potential-energy-efficiency': '88',
'total-floor-area': '73',
'construction-age-band': 'England and Wales: 1930-1949',
'property-type': 'House',
'built-form': 'Mid-Terrace',
}
]
# This is information that is found as a result of the non-invasives, that mean that certain measures
# have been installed already. To reflect this in the front end, it is included in the recommendation, however
# the cost is removed and instead, a message is presented saying that the measure is already installed.
already_installed = [
{
'address': '28 Sangwin Road', 'postcode': 'WV14 9EQ', "already_installed": ["loft_insulation"]
},
{
'address': '51 Hillwood Road', 'postcode': 'B62 8NQ', "already_installed": ["loft_insulation"]
},
{
'address': '47 Watsons Close', 'postcode': 'DY2 7HL', "already_installed": ["loft_insulation"]
},
{
'address': '44 Hatfield Road',
'postcode': 'DY9 7LW',
"already_installed": ["loft_insulation", "cavity_wall_insulation"]
}
]
non_invasive_recommendations = []
def app():
raw_asset_list = read_excel_from_s3(
bucket_name="retrofit-datalake-dev",
file_key="customers/Immo/Dudley Asset List - Hestia - pilot2.xlsx",
header_row=0
)
raw_asset_list = raw_asset_list[raw_asset_list["in_pilot"]].copy()
# Extract address and postcode
raw_asset_list["address"] = raw_asset_list["Full Address"].str.split(",").str[0]
raw_asset_list["postcode"] = raw_asset_list["Full Address"].str.split(",").str[-1].str.strip()
# We're provided with number of bathrooms and number of bedrooms.
# THe UPRNs are not the official ones
asset_list = raw_asset_list.rename(
columns={
"No. of Beds": "n_bedrooms",
"No. of WC's": "n_bathrooms",
'Property Type': 'property_type',
'Architype': 'built_form'
}
)
# Remap the values
asset_list["built_form"] = asset_list["built_form"].map({
"SEMI DETACHED": "Semi-Detached",
"MID TERRACE": "Mid-Terrace",
"END TERRACE": "End-Terrace",
})
# 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
)
# Store overrides in s3
already_installed_filename = f"{USER_ID}/{PORTFOLIO_ID}/already_installed.json"
save_csv_to_s3(
dataframe=pd.DataFrame(already_installed),
bucket_name="retrofit-plan-inputs-dev",
file_name=already_installed_filename
)
# Store patches in s3
patches_filename = f"{USER_ID}/{PORTFOLIO_ID}/patches.json"
save_csv_to_s3(
dataframe=pd.DataFrame(patches),
bucket_name="retrofit-plan-inputs-dev",
file_name=patches_filename
)
# Store non-invasive recommendations in S3
non_invasive_recommendations_filename = f"{USER_ID}/{PORTFOLIO_ID}/non_invasive_recommendations.json"
save_csv_to_s3(
dataframe=pd.DataFrame(non_invasive_recommendations),
bucket_name="retrofit-plan-inputs-dev",
file_name=non_invasive_recommendations_filename
)
# EPC C portoflio
body = {
"portfolio_id": str(PORTFOLIO_ID),
"housing_type": "Private",
"goal": "Increase EPC",
"goal_value": "C",
"trigger_file_path": filename,
"already_installed_file_path": already_installed_filename,
"patches_file_path": patches_filename,
"non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
"budget": None,
}
print(body)
# EPC B portoflio
body = {
"portfolio_id": str(PORTFOLIO_ID + 1),
"housing_type": "Private",
"goal": "Increase EPC",
"goal_value": "B",
"trigger_file_path": filename,
"already_installed_file_path": already_installed_filename,
"patches_file_path": patches_filename,
"non_invasive_recommendations_file_path": non_invasive_recommendations_filename,
"budget": None,
}
print(body)

2
etl/epc/Record.py
View file

@ -191,7 +191,7 @@ class EPCRecord:
This method will clean the records using the data processor
"""
epc_data_processor = EPCDataProcessor(
data=self.epc_record_as_dataframe("prepared_epc"),
data=self.epc_record_as_dataframe("prepared_epc").copy(),
run_mode="newdata",
cleaning_averages=self.cleaning_data,
)

12
recommendations/Costs.py
View file

@ -67,18 +67,12 @@ LOW_CARBON_COMBI_BOILER = 2200
# https://www.greenmatch.co.uk/boilers/35kw-boiler
# https://www.greenmatch.co.uk/boilers/40kw-boiler
# These are exclusive of installation costs
COMBI_BOILER_COSTS = {
CONDENSING_BOILER_COSTS = {
"30kw": 1550,
"35kw": 1610,
"40kw": 1625
}
CONVENTIONAL_BOILER_COSTS = {
"30kw": 1117,
"35kw": 1546,
"40kw": 1776
}
# Assumes 3 hours to remove each heater (including re-decorating)
ROOM_HEATER_REMOVAL_COST = 120
ROOM_HEATER_REMOVAL_LABOUR_HOURS = 3
@ -1179,7 +1173,7 @@ class Costs:
estimated_radiators = max(total_radiators_based_on_power, base_radiators + additional_radiators)
return round(estimated_radiators)
def boiler(self, is_combi, size, exising_room_heaters, system_change, n_heated_rooms, n_rooms):
def boiler(self, size, exising_room_heaters, system_change, n_heated_rooms, n_rooms):
"""
Based on a basic estimate of median value £2600 to install a low carbon combi boiler
First time central heating vosts can als be found here:
@ -1187,7 +1181,7 @@ class Costs:
:return:
"""
unit_cost = COMBI_BOILER_COSTS[size] if is_combi else CONVENTIONAL_BOILER_COSTS[size]
unit_cost = CONDENSING_BOILER_COSTS[size]
# The unit cost is the cost without VAT
# We now need to estimate the cost of the works
labour_days = 2

51
recommendations/HeatingRecommender.py
View file

@ -15,7 +15,8 @@ class HeatingRecommender:
self.property = property_instance
self.costs = Costs(self.property)
self.recommendations = []
self.heating_recommendations = []
self.heating_control_recommendations = []
def recommend(self, phase=0):
@ -23,7 +24,8 @@ class HeatingRecommender:
# the boiler, but instead flushing the system will make it run more efficiently. There is a cost for this
# in the Costs class, stored as SYSTEM_FLUSH_COST
self.recommendations = []
self.heating_recommendations = []
self.heating_control_recommendations = []
# This first iteration of the recommender will provide very basic recommendation
# We recommend heating controls based on the main heating system
@ -254,7 +256,7 @@ class HeatingRecommender:
system_change=system_change
)
self.recommendations.extend(recommendations)
self.heating_recommendations.extend(recommendations)
@staticmethod
def estimate_boiler_size(property_type, built_form, floor_area, floor_height, num_heated_rooms):
@ -312,7 +314,15 @@ class HeatingRecommender:
simulation_config = {}
boiler_costs = {}
boiler_recommendation = {}
if self.property.data["mainheat-energy-eff"] in ["Very Poor", "Poor", "Average"]:
has_inefficient_space_heating = self.property.data["mainheat-energy-eff"] in ["Very Poor", "Poor", "Average"]
has_inefficient_mains_water = (
self.property.hotwater["clean_description"] in ["From main system"] and
self.property.data["hot-water-energy-eff"] in ["Very Poor", "Poor", "Average"]
)
if has_inefficient_space_heating or has_inefficient_mains_water:
boiler_size = self.estimate_boiler_size(
property_type=self.property.data["property-type"],
built_form=self.property.data["built-form"],
@ -321,22 +331,12 @@ class HeatingRecommender:
num_heated_rooms=self.property.data["number-heated-rooms"],
)
# We recommend a combi boiler under the following conditions
# 1) If there are 4 or fewer rooms (we don't use heqted rooms because none of the rooms could be
# heated if there is no existing heating system).
# 2) There 1 or fewer bathrooms
# Otherwise, we recommend a gas condensing boiler, which will server a larger property, that has multiple
# bathrooms
is_combi = (
(self.property.number_of_rooms <= 4) and
(self.property.n_bathrooms in [None, 0, 1])
)
if is_combi:
description = "Upgrade to a new combi boiler"
else:
description = "Upgrade to a new gas condensing boiler"
description = "Upgrade to a new condensing boiler"
simulation_config = {"mainheat_energy_eff_ending": "Good"}
simulation_config = {
"mainheat_energy_eff_ending": "Good",
"hot_water_energy_eff_ending": "Good"
}
if system_change:
# Installation of a boiler improves the hot water system so we need to reflect this in
# the outcome of the recommendation
@ -363,7 +363,6 @@ class HeatingRecommender:
}
boiler_costs = self.costs.boiler(
is_combi=is_combi,
size=f"{boiler_size}kw",
exising_room_heaters=exising_room_heaters,
system_change=system_change,
@ -397,9 +396,13 @@ class HeatingRecommender:
controls_recommender.recommend(heating_description="Boiler and radiators, mains gas")
# We may have 2 recommendations from the heating controls
if not controls_recommender.recommendation:
if not controls_recommender.recommendation and not boiler_recommendation:
return
if not system_change and len(boiler_recommendation):
# If there is not a system change, we add the boiler recommendation at point.
self.heating_recommendations.extend([boiler_recommendation])
if system_change:
# We combine the heating and controls recommendations, in the case of a system change
combined_recommendations = []
@ -416,12 +419,12 @@ class HeatingRecommender:
combined_recommendations.extend(combined_recommendation)
# Overwrite the existing boiler recommendation
self.recommendations.extend(combined_recommendations)
self.heating_recommendations.extend(combined_recommendations)
else:
# We increment the recommendation phase, since the heating controls are separate from the boiler upgrade
# but we'll only upgrade if we have a heating recommendation
has_heating_recommendation = any(
recommendation["type"] == "heating" for recommendation in self.recommendations
rec["type"] == "heating" for rec in self.heating_recommendations
)
if has_heating_recommendation:
recommendation_phase += 1
@ -430,6 +433,6 @@ class HeatingRecommender:
for recommendation in controls_recommender.recommendation:
recommendation["phase"] = recommendation_phase
self.recommendations.extend(controls_recommender.recommendation)
self.heating_control_recommendations.extend(controls_recommender.recommendation)
return

18
recommendations/Recommendations.py
View file

@ -110,12 +110,24 @@ class Recommendations:
# Heating and Electical systems
if "heating" not in self.exclusions:
self.heating_recommender.recommend(phase=phase)
if self.heating_recommender.recommendations:
property_recommendations.append(self.heating_recommender.recommendations)
if (
self.heating_recommender.heating_recommendations or
self.heating_recommender.heating_control_recommendations
):
if self.heating_recommender.heating_recommendations:
property_recommendations.append(self.heating_recommender.heating_recommendations)
if self.heating_recommender.heating_control_recommendations:
property_recommendations.append(self.heating_recommender.heating_control_recommendations)
# We check if we have distinct heating and heating controls recommendations
# If so, we increment by 2 (one of the heating system, one for the heating controls)
# otherwise we incremenet by 1
max_used_phase = max([rec["phase"] for rec in self.heating_recommender.recommendations])
max_used_phase = max(
[rec["phase"] for rec in
self.heating_recommender.heating_recommendations +
self.heating_recommender.heating_control_recommendations]
)
amount_to_increment = max_used_phase - phase + 1
phase += amount_to_increment

10
recommendations/SolarPvRecommendations.py
View file

@ -56,14 +56,18 @@ class SolarPvRecommendations:
if not is_valid_property_type or not is_valid_roof_type or not has_no_existing_solar_pv:
return
solar_pv_percentage = self.property.solar_pv_percentage
# We round up to the neaest 10%
solar_pv_percentage = np.ceil(solar_pv_percentage * 10) / 10
# For the solar recommendations, we produce the following scenarios:
# 1) Solar panels only, we present a high, medium and low coverage
# 2) With and without battery
roof_coverage_scenarios = [
self.property.solar_pv_percentage - 0.1, self.property.solar_pv_percentage,
solar_pv_percentage - 0.1, solar_pv_percentage,
]
if self.property.solar_pv_percentage <= 0.4:
roof_coverage_scenarios.append(self.property.solar_pv_percentage + 0.1)
if solar_pv_percentage <= 0.4:
roof_coverage_scenarios.append(solar_pv_percentage + 0.1)
# We make sure we haven't gone too low or high - we allow no more than 60% coverage
roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 0.6]
# If we only have two scenarios, we add a coverage scenario 10% less than the smallest