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wip integrating additional recs with new code

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This commit is contained in:
Khalim Conn-Kowlessar 2024-07-11 08:35:34 +01:00
parent 050981ae82
commit b20e00a736
10 changed files with 336 additions and 29 deletions
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21
backend/Property.py
View file

@ -357,6 +357,27 @@ class Property:
for config in epc_transformations:
for k, v in config.items():
if k in phase_epc_transformation:
if "-energy-eff" in k:
# We take the highest value
if phase_epc_transformation[k] == "Very Good":
continue
elif phase_epc_transformation[k] == "Good":
if v == "Very Good":
phase_epc_transformation[k] = v
elif phase_epc_transformation[k] == "Average":
if v in ["Good", "Very Good"]:
phase_epc_transformation[k] = v
elif phase_epc_transformation[k] == "Poor":
if v in ["Average", "Good", "Very Good"]:
phase_epc_transformation[k] = v
else:
phase_epc_transformation[k] = v
continue
if phase_epc_transformation[k] == v:
continue
raise NotImplementedError(
"Already have this key in the phase_epc_transformation - implement me")
phase_epc_transformation[k] = v

37
backend/apis/GoogleSolarApi.py
View file

@ -7,6 +7,7 @@ from functools import lru_cache
import time
from backend.app.db.functions.solar_functions import get_solar_data, store_batch_data
from utils.logger import setup_logger
from sklearn.preprocessing import MinMaxScaler
logger = setup_logger()
@ -198,6 +199,7 @@ class GoogleSolarApi:
scenarios_data["scenario_type"] = scenario_type
scenarios_data = scenarios_data.to_dict(orient="records")
# TODO: Rather than just doing a straight insert, we should overwrite what's already there if it exists
store_batch_data(
session=session,
api_data=self.insights_data,
@ -244,7 +246,7 @@ class GoogleSolarApi:
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)
cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (wattage / 1000)
roi_summary.append(
{
"segmentIndex": segment["segmentIndex"],
@ -309,17 +311,19 @@ class GoogleSolarApi:
)
# Now that we know the lifetime cnsumption of ac kwh, we can estimate the roi
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"]
lifetime_energy_consumption = energy_consumption * self.installation_life_span
surplus = 0
if lifetime_ac_kwh < lifetime_energy_consumption:
# We estimate the amount of electricity generated, based on the price cap
generation_value = lifetime_ac_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP
roi = generation_value / panel_config["total_cost"]
generation_deficit = lifetime_energy_consumption - lifetime_ac_kwh
else:
# We now have a surplus of energy, which we can sell back to the grid
surplus = lifetime_ac_kwh - lifetime_energy_consumption
surplus_value = surplus * AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
@ -341,7 +345,8 @@ class GoogleSolarApi:
"roi": roi,
"generation_value": generation_value,
"generation_deficit": generation_deficit,
"expected_payback_years": expected_payback_years
"expected_payback_years": expected_payback_years,
"surplus": surplus
}
)
@ -351,12 +356,28 @@ class GoogleSolarApi:
roi_results, how="left", on="n_panels"
)
# We prioritise maximal roi, then minimal geneartion deficit, then maximal generation value (if there is still
# a tie). Ideally, we want the best roi over the lifetime of the solar panels, but we also want to ensure that
# we can meet the energy demands of the building.
panel_performance = panel_performance.sort_values(
["roi", "generation_deficit", "generation_value"], ascending=[False, True, False]
# We want max roi, minimal generation deficit, and max generation value - we create a ranking score
# Assign equal weights to each metric
weights = {'roi': 0.6, 'generation_value': 0.2, 'generation_deficit': 0.2}
metrics = panel_performance[['roi', 'generation_value', 'generation_deficit']]
# Normalize the columns (0 to 1 scale)
scaler = MinMaxScaler()
normalized_metrics = scaler.fit_transform(metrics)
# Convert normalized metrics back to a dataframe
normalized_metrics_df = pd.DataFrame(
normalized_metrics, columns=['roi', 'generation_value', 'generation_deficit']
)
normalized_metrics_df['combined_score'] = (
normalized_metrics_df['roi'] * weights['roi'] +
normalized_metrics_df['generation_value'] * weights['generation_value'] +
(1 - normalized_metrics_df['generation_deficit']) * weights['generation_deficit']
)
panel_performance['combined_score'] = normalized_metrics_df['combined_score'].values
panel_performance['rank'] = panel_performance['combined_score'].rank(ascending=False)
panel_performance = panel_performance.sort_values(by='rank')
panel_performance["expected_payback_years"] = np.ceil(panel_performance["expected_payback_years"]).astype(int)

1
backend/app/plan/router.py
View file

@ -431,6 +431,7 @@ async def trigger_plan(body: PlanTriggerRequest):
}
# Store the data in the database
# TODO: Rather than just doing a straight insert, we should overwrite what's already there if it exists
solar_api_client.save_to_db(
session=session, uprns_to_location=building_uprns[building_id], scenario_type="building"
)

176
etl/customers/places_for_people/demo_portfolio.py
View file

@ -3,6 +3,7 @@ import pandas as pd
from utils.s3 import save_csv_to_s3
PORTFOLIO_ID = 83
SECOND_PORTFOLIO_ID = 84
USER_ID = 8
@ -67,6 +68,181 @@ def app():
"patches_file_path": "",
"non_invasive_recommendations_file_path": "",
"budget": None,
"exclusions": ["floor_insulation"]
}
print(body)
# Get an example of flats with solar panels from epc data
# 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"
#
# epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
#
# directory = epc_directories[1]
# data = pd.read_csv(directory / "certificates.csv", low_memory=False)
# # Get flats
# data = data[data["PROPERTY_TYPE"].str.lower().str.contains("flat")]
# data = data[~pd.isnull(data["UPRN"])]
# data["UPRN"] = data["UPRN"].astype(int).astype(str)
# data = data[pd.to_datetime(data["LODGEMENT_DATE"]) > "2020-01-01"]
# flats_with_solar = data[data['PHOTO_SUPPLY'] > 0]
#
# print(flats_with_solar["UPRN"])
#
# flats_with_solar[["ADDRESS", "UPRN"]]
#
# # Good example:
# # UPRN: 10013160824, Flat 39, The Meadow, 30 Busk Meadow S5 7JH (care home with 39 flats, have solar panels)
# #
# # Mostly, For a mid-floor flat, the property doesn't show as having solar panels through the photo_supply variable
# # But actually for UPRN: 10013245713, Apartment 4, Orchard House, Gill Lane PR4 5QN, this has a dwelling above
# # but the photo_supply variable is 20
#
# # Small flat consisting of 2 units
# # UPRN: 42172953, FLAT 2, 276 CLAUGHTON ROAD, BIRKENHEAD CH41 4DX
#
# # Flat containing 5 units
# # UPRN: 10013247127 Flat 1, Old Church House PR4 5GE
# # UPRN: 10013247130 Flat 4, Old Church House PR4 5GE
#
# # Flat containing multiple units:
# # UPRNS: 10013245710, 10013245716, 10013245711, 10013245717, 10013245714, 10013245715, 10013245712, 10013245713
#
# # Look for flats with air source heat pumps!
# flats_with_asps = data[data["MAINHEAT_DESCRIPTION"].str.lower().str.contains("air source heat pump")]
# print(flats_with_asps[["UPRN", "ADDRESS"]])
def app_epc_b():
# TODO: We can insert a variable, indicating the they own all of the units in the building
asset_list = [
{
"address": "Flat 1, Fenton Court",
"postcode": "N2 8DS",
"uprn": 200140644,
"building_id": 1,
},
{
"address": "Flat 2, Fenton Court",
"postcode": "N2 8DS",
"uprn": 200140645,
"building_id": 1,
},
{
"address": "Flat 3, Fenton Court",
"postcode": "N2 8DS",
"uprn": 200140646,
"building_id": 1,
},
{
"address": "Flat 4, Fenton Court",
"postcode": "N2 8DS",
"uprn": 200140647,
"building_id": 1,
},
{
"address": "Flat 5, Fenton Court",
"postcode": "N2 8DS",
"uprn": 200140648,
"building_id": 1,
},
{
"address": "Flat 6, Fenton Court",
"postcode": "N2 8DS",
"uprn": 200140649,
"building_id": 1,
}
]
non_invasive_recommendations = [
{
"address": "Flat 1, Fenton Court",
"postcode": "N2 8DS",
'recommendations': [
'cavity_extract_and_refill',
# 'air_source_heat_pump'
]
},
{
"address": "Flat 2, Fenton Court",
"postcode": "N2 8DS",
'recommendations': [
'cavity_extract_and_refill',
# 'air_source_heat_pump'
]
},
{
"address": "Flat 3, Fenton Court",
"postcode": "N2 8DS",
'recommendations': [
'cavity_extract_and_refill',
# 'air_source_heat_pump'
]
},
{
"address": "Flat 4, Fenton Court",
"postcode": "N2 8DS",
'recommendations': [
'cavity_extract_and_refill',
# 'air_source_heat_pump'
]
},
{
"address": "Flat 5, Fenton Court",
"postcode": "N2 8DS",
'recommendations': [
'cavity_extract_and_refill',
'loft_insulation',
# 'air_source_heat_pump'
]
},
{
"address": "Flat 6, Fenton Court",
"postcode": "N2 8DS",
'recommendations': [
'cavity_extract_and_refill',
'loft_insulation',
# 'air_source_heat_pump'
]
},
]
asset_list = pd.DataFrame(asset_list)
# Store the asset list in s3
filename = f"{USER_ID}/{SECOND_PORTFOLIO_ID}/non_intrusives.csv"
save_csv_to_s3(
dataframe=asset_list,
bucket_name="retrofit-plan-inputs-dev",
file_name=filename
)
# Store non-invasive recommendations in S3
non_invasive_recommendations_filename = f"{USER_ID}/{SECOND_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
)
body = {
"portfolio_id": str(SECOND_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": non_invasive_recommendations_filename,
"budget": None,
"exclusions": ["floor_insulation"]
}
print(body)

30
recommendations/Costs.py
View file

@ -18,23 +18,23 @@ regional_labour_variations = [
{"Region": "Northern Ireland", "Adjustment_Factor": 0.76}
]
# This data is based on the MCS database
# This data is based on the MCS database - taken the figures for June 2024
MCS_SOLAR_PV_COST_DATA = {
"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,
"last_updated": "2024-07-10",
"average_cost_per_kwh": 1825,
"average_cost_per_kwh-Outer London": 1950,
"average_cost_per_kwh-Inner London": 1950,
"average_cost_per_kwh-South East England": 1966,
"average_cost_per_kwh-South West England": 1864,
"average_cost_per_kwh-East of England": 1719,
"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,
"average_cost_per_kwh-West Midlands": 1789,
"average_cost_per_kwh-North East England": 1872,
"average_cost_per_kwh-North West England": 1860,
"average_cost_per_kwh-Yorkshire and the Humber": 1789,
"average_cost_per_kwh-Wales": 1676,
"average_cost_per_kwh-Scotland": 1781,
"average_cost_per_kwh-Northern Ireland": 1347,
}
# This data is based on the MCS database, We use the larger figure between the 2023 and 2024 average,

21
recommendations/HeatingRecommender.py
View file

@ -183,6 +183,10 @@ class HeatingRecommender:
"boiler")
def is_ashp_valid(self):
if "air_source_heat_pump" in self.property.non_invasive_recommendations:
return True
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"]
@ -232,6 +236,12 @@ class HeatingRecommender:
"mainheat_energy_eff_ending": "Good",
"hot_water_energy_eff_ending": "Good"
}
description_simulation = {
"mainheat-description": "Air source heat pump, radiators, electric",
"mainheat-energy-eff": "Good",
"hot-water-energy-eff": "Good",
"hotwater-description": "From main system",
}
# Installation of a boiler improves the hot water system so we need to reflect this in
# the outcome of the recommendation
heating_ending_config = MainHeatAttributes("Air source heat pump, radiators, electric").process()
@ -241,6 +251,10 @@ class HeatingRecommender:
fuel_ending_config = {}
if self.property.main_fuel["fuel_type"] != "electricity":
fuel_ending_config = MainFuelAttributes("electricity (not community)").process()
description_simulation = {
**description_simulation,
"main-fuel": "electricity (not community)"
}
# Check the simulation differences
heating_simulation_config = check_simulation_difference(
@ -270,6 +284,12 @@ class HeatingRecommender:
**controls_recommender.recommendation[0]["simulation_config"]
}
description_simulation = {
**description_simulation,
"mainheatcont-description": "time and temperature zone control",
"mainheatc-energy-eff": "Very Good"
}
ashp_recommendation = {
"phase": phase,
"parts": [
@ -282,6 +302,7 @@ class HeatingRecommender:
"sap_points": None,
"already_installed": already_installed,
"simulation_config": simulation_config,
"description_simulation": description_simulation,
**ashp_costs
}

17
recommendations/Recommendations.py
View file

@ -752,6 +752,23 @@ class Recommendations:
predicted_appliances_kwh_reduction
)
# We store this value for later
phase_lighting_costs[rec["phase"]] = {
"adjusted": new_lighting_cost,
"unadjusted": scoring_lighting_cost
}
phase_kwh_figures[rec["phase"]] = {
"adjusted": {
"heating": new_heating_kwh_adjusted,
"hot_water": new_hot_water_kwh_adjusted
},
"unadjusted": {
"heating": new_heating_kwh,
"hot_water": new_hot_water_kwh
}
}
if rec["type"] == "low_energy_lighting":
# For the moment, we cap the number of SAP points that can be achieved by ventilation at 2
rec["sap_points"] = min(predicted_sap_points, LightingRecommendations.SAP_LIMIT)

52
recommendations/RoofRecommendations.py
View file

@ -23,6 +23,7 @@ class RoofRecommendations:
# It is recommended that lofts should have at least 270mm of insulation. If the property has more than 200mm of
# loft insulation in place already, we do not recommend anything for the moment
MINIMUM_LOFT_ISULATION_MM = 200
MINIMUM_RECOMMENDED_LOFT_INSULATION = 280
# Flat roof should have at least 100mm of insulation
MINIMUM_FLAT_ROOF_ISULATION_MM = 100
@ -79,6 +80,11 @@ class RoofRecommendations:
"""
Check if the loft is already insulated
"""
# If we have a non-invasive recommendation for the loft insulation, we can assume that the loft is not insulated
if "loft_insulation" in self.property.non_invasive_recommendations:
return False
return (self.insulation_thickness > self.MINIMUM_LOFT_ISULATION_MM) and self.property.roof["is_pitched"]
def recommend(self, phase):
@ -115,12 +121,17 @@ class RoofRecommendations:
u_value = get_roof_u_value(**{**self.property.roof, "age_band": self.property.age_band})
self.estimated_u_value = u_value
if u_value <= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE:
if (u_value <= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE) and (
"loft_insulation" not in self.property.non_invasive_recommendations
):
# The Roof is already compliant
return
if self.property.roof["is_pitched"] or self.property.roof["is_flat"]:
self.recommend_roof_insulation(u_value, self.insulation_thickness, self.property.roof, phase)
insulation_thickness = (
0 if "loft_insulation" not in self.property.non_invasive_recommendations else self.insulation_thickness
)
self.recommend_roof_insulation(u_value, insulation_thickness, self.property.roof, phase)
return
if self.property.roof["is_roof_room"]:
@ -200,7 +211,9 @@ class RoofRecommendations:
# We make sure we hit a depth of 270mm. We should factor in any existing insulation if the
# loft is already partially insulated.
# Note: This requirement is only for loft insulation
if ((material["depth"] + insulation_thickness) < self.MINIMUM_LOFT_ISULATION_MM) and roof["is_pitched"]:
if (
(material["depth"] + insulation_thickness) < self.MINIMUM_RECOMMENDED_LOFT_INSULATION
) and roof["is_pitched"]:
continue
part_u_value = r_value_per_mm_to_u_value(material["depth"], material["r_value_per_mm"])
@ -245,6 +258,35 @@ class RoofRecommendations:
else:
raise ValueError("Invalid material type")
# This is based on the values we have in the training data
valid_numeric_values = [
12,
25,
50,
75,
100,
150,
200,
250,
270,
300,
350,
400,
]
proposed_depth = new_thickness
if new_thickness not in valid_numeric_values:
# Take the nearest value for scoring
proposed_depth = min(
valid_numeric_values, key=lambda x: abs(x - proposed_depth)
)
if proposed_depth >= 270:
new_efficiency = "Very Good"
else:
if self.property.data["walls-energy-eff"] not in ["Good", "Very Good"]:
new_efficiency = "Good"
recommendations.append(
{
"phase": phase,
@ -263,6 +305,10 @@ class RoofRecommendations:
"sap_points": None,
"already_installed": already_installed,
"new_thickness": new_thickness,
"description_simulation": {
"roof-description": f"Pitched, {int(proposed_depth)}mm loft insulation",
"roof-energy-eff": new_efficiency
},
**cost_result
}
)

2
recommendations/VentilationRecommendations.py
View file

@ -72,7 +72,7 @@ class VentilationRecommendations(Definitions):
"already_installed": already_installed,
"sap_points": 0,
"heat_demand": 0,
"adjusted_heat_demand": 0,
"kwh_savings": 0,
"co2_equivalent_savings": 0,
"energy_cost_savings": 0,
"total": estimated_cost,

8
recommendations/WallRecommendations.py
View file

@ -252,7 +252,7 @@ class WallRecommendations(Definitions):
self.estimated_u_value = u_value
if is_cavity_wall:
if is_cavity_wall or "cavity_extract_and_refill" in self.property.non_invasive_recommendations:
if u_value >= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE:
# Test filling cavity
self.find_cavity_insulation(u_value, insulation_thickness, phase)
@ -357,7 +357,7 @@ class WallRecommendations(Definitions):
simulation_config = {
**simulation_config,
**walls_simulation_config,
"walls_thermal_transmittance_ending": new_u_value
"walls_thermal_transmittance_ending": new_u_value,
}
recommendations.append(
@ -378,6 +378,10 @@ class WallRecommendations(Definitions):
"sap_points": None,
"already_installed": already_installed,
"simulation_config": simulation_config,
"description_simulation": {
"walls-description": "Cavity wall, filled cavity",
"walls-energy-eff": "Good"
},
**cost_result
}
)