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removed low carbon from boiler terminology

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This commit is contained in:
Khalim Conn-Kowlessar 2024-04-04 16:35:14 +01:00
parent f5961ff6c7
commit 93830f90bb
3 changed files with 431 additions and 14 deletions
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6
backend/ml_models/AnnualBillSavings.py
View file

@ -19,7 +19,8 @@ class AnnualBillSavings:
PRICE_FACTOR = 0.09549999999999999
# Daily standard charge, based on average across England, Scotland and Wales, and includes VAT
DAILY_STANDARD_CHARGE = 0.3143
DAILY_STANDARD_CHARGE_GAS = 0.3143
DAILY_STANDARD_CHARGE_ELECTRICITY = 0.601
EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
@ -45,11 +46,12 @@ class AnnualBillSavings:
def calculate_annual_bill(cls, kwh):
"""
This method will estimate the total annual bill for a property
It assumed gas & electricity are used
:param kwh: The total kwh consumption
:return: An estimate for annual bill
"""
return cls.PRICE_FACTOR * kwh + cls.DAILY_STANDARD_CHARGE * 365
return cls.PRICE_FACTOR * kwh + (cls.DAILY_STANDARD_CHARGE_GAS + cls.DAILY_STANDARD_CHARGE_ELECTRICITY * 365)
@classmethod
def adjust_energy_to_metered(cls, epc_energy_consumption, current_epc_rating):

424
etl/customers/gla_croydon_demo/slides.py
View file

@ -112,6 +112,49 @@ def scenario_1():
recommendations_summary["total_bill_savings"] / recommendations_summary["current_energy_bill"]
)
########################
# Overview
########################
overview_totals = recommendations_summary.sum()
overview_means = recommendations_summary.mean()
########################
# Measures
########################
measures_count = recommendations_df.groupby("type")["id"].count().reset_index()
wall_insulation_measures = measures_count[
measures_count["type"].isin(["cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation"])
]["id"].sum()
ventilation_measures = measures_count[
measures_count["type"].isin(["mechanical_ventilation"])
]["id"].sum()
roof_insulation_measures = measures_count[
measures_count["type"].isin(["loft_insulation", "flat_roof_insulation"])
]["id"].sum()
floor_insulation_measures = measures_count[
measures_count["type"].isin(["solid_floor_insulation", "suspended_floor_insulation"])
]["id"].sum()
windows = measures_count[
measures_count["type"].isin(["windows_glazing"])
]["id"].sum()
heating = measures_count[
measures_count["type"].isin(["heating"])
]["id"].sum()
heating_controls = measures_count[
measures_count["type"].isin(["heating_control"])
]["id"].sum()
solar = measures_count[
measures_count["type"].isin(["solar_pv"])
]["id"].sum()
other = measures_count[
~measures_count["type"].isin([
"cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation",
"loft_insulation", "flat_roof_insulation", "solid_floor_insulation",
"suspended_floor_insulation", "windows_glazing", "heating", "heating_control", "solar_pv",
"mechanical_ventilation"
])
]["id"].sum()
# Summary information by each archetype
########################
# Archetype 1
@ -121,10 +164,54 @@ def scenario_1():
recommendations_summary["uprn"].astype(str).isin(archetype_1["uprn"].values)
]
arch_1_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_1["uprn"].values)
]
arch_1_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_1_recommendation_min = recommendations_arch_1_summary.min()
arch_1_recommendation_max = recommendations_arch_1_summary.max()
arch_1_recommendation_means = recommendations_arch_1_summary.mean()
cols_to_keep = ["total_cost", "total_carbon", "total_bill_savings", "total_sap_points", "adjusted_heat_demand",
"energy_percent_change", "carbon_percent_change", "bills_percent_change"]
arch_1_recommendation_min = recommendations_arch_1_summary.min()[cols_to_keep]
arch_1_recommendation_max = recommendations_arch_1_summary.max()[cols_to_keep]
arch_1_recommendation_means = recommendations_arch_1_summary.mean()[cols_to_keep]
arch_1_totals = recommendations_arch_1_summary.sum()[cols_to_keep]
annual_total_co2 = recommendations_arch_1_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_1_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_1_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_1["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_1_recommendation_means['total_cost'], 2)}: "
f"{arch_1_recommendation_min['total_cost']} - {arch_1_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_1_recommendation_means['total_sap_points'], 2)}: "
f"{arch_1_recommendation_min['total_sap_points']} - {arch_1_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_1_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_1_recommendation_min['adjusted_heat_demand']} - "
f"{arch_1_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_1_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_1_recommendation_min['energy_percent_change']} - "
f"{arch_1_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_1_recommendation_means['total_carbon'], 2)}: "
f"{arch_1_recommendation_min['total_carbon']} - {arch_1_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_1_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_1_recommendation_min['carbon_percent_change']} - "
f"{arch_1_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_1_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_1_recommendation_min['total_bill_savings']} - "
f"{arch_1_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_1_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_1_recommendation_min['bills_percent_change']} - "
f"{arch_1_recommendation_max['bills_percent_change']}")
########################
# Archetype 2
@ -134,11 +221,53 @@ def scenario_1():
recommendations_summary["uprn"].astype(str).isin(archetype_2["uprn"].values)
]
arch_2_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_2["uprn"].values)
]
arch_2_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_2_recommendation_min = recommendations_arch_2_summary.min()
arch_2_recommendation_max = recommendations_arch_2_summary.max()
arch_2_recommendation_means = recommendations_arch_2_summary.mean().round(2)
total_cost = recommendations_arch_2_summary["total_cost"].sum()
annual_total_co2 = recommendations_arch_2_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_2_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_2_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_2["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_2_recommendation_means['total_cost'], 2)}: "
f"{arch_2_recommendation_min['total_cost']} - {arch_2_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_2_recommendation_means['total_sap_points'], 2)}: "
f"{arch_2_recommendation_min['total_sap_points']} - {arch_2_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_2_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_2_recommendation_min['adjusted_heat_demand']} - "
f"{arch_2_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_2_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_2_recommendation_min['energy_percent_change']} - "
f"{arch_2_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_2_recommendation_means['total_carbon'], 2)}: "
f"{arch_2_recommendation_min['total_carbon']} - {arch_2_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_2_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_2_recommendation_min['carbon_percent_change']} - "
f"{arch_2_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_2_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_2_recommendation_min['total_bill_savings']} - "
f"{arch_2_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_2_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_2_recommendation_min['bills_percent_change']} - "
f"{arch_2_recommendation_max['bills_percent_change']}")
########################
# Archetype 3
########################
@ -147,11 +276,53 @@ def scenario_1():
recommendations_summary["uprn"].astype(str).isin(archetype_3["uprn"].values)
]
arch_3_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_3["uprn"].values)
]
arch_3_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_3_recommendation_min = recommendations_arch_3_summary.min()
arch_3_recommendation_max = recommendations_arch_3_summary.max()
arch_3_recommendation_means = recommendations_arch_3_summary.mean()
total_cost = recommendations_arch_3_summary["total_cost"].sum()
annual_total_co2 = recommendations_arch_3_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_3_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_3_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_3_recommendation_means['total_cost'], 2)}: "
f"{arch_3_recommendation_min['total_cost']} - {arch_3_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_3_recommendation_means['total_sap_points'], 2)}: "
f"{arch_3_recommendation_min['total_sap_points']} - {arch_3_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_3_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_3_recommendation_min['adjusted_heat_demand']} - "
f"{arch_3_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_3_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_3_recommendation_min['energy_percent_change']} - "
f"{arch_3_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_3_recommendation_means['total_carbon'], 2)}: "
f"{arch_3_recommendation_min['total_carbon']} - {arch_3_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_3_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_3_recommendation_min['carbon_percent_change']} - "
f"{arch_3_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_3_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_3_recommendation_min['total_bill_savings']} - "
f"{arch_3_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_3_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_3_recommendation_min['bills_percent_change']} - "
f"{arch_3_recommendation_max['bills_percent_change']}")
########################
# Archetype 4
########################
@ -160,14 +331,52 @@ def scenario_1():
recommendations_summary["uprn"].astype(str).isin(archetype_4["uprn"].values)
]
arch_4_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
]
arch_4_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_4_recommendation_min = recommendations_arch_4_summary.min()
arch_4_recommendation_max = recommendations_arch_4_summary.max()
arch_4_recommendation_means = recommendations_arch_4_summary.mean()
property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
]["total_floor_area"].mean()
total_cost = recommendations_arch_4_summary["total_cost"].sum()
annual_total_co2 = recommendations_arch_4_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_4_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_4_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_4["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_4_recommendation_means['total_cost'], 2)}: "
f"{arch_4_recommendation_min['total_cost']} - {arch_4_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_4_recommendation_means['total_sap_points'], 2)}: "
f"{arch_4_recommendation_min['total_sap_points']} - {arch_4_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_4_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_4_recommendation_min['adjusted_heat_demand']} - "
f"{arch_4_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_4_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_4_recommendation_min['energy_percent_change']} - "
f"{arch_4_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_4_recommendation_means['total_carbon'], 2)}: "
f"{arch_4_recommendation_min['total_carbon']} - {arch_4_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_4_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_4_recommendation_min['carbon_percent_change']} - "
f"{arch_4_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_4_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_4_recommendation_min['total_bill_savings']} - "
f"{arch_4_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_4_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_4_recommendation_min['bills_percent_change']} - "
f"{arch_4_recommendation_max['bills_percent_change']}")
########################
# Overview
@ -291,6 +500,38 @@ def scenario_2():
# Measures
########################
measures_count = recommendations_df.groupby("type")["id"].count().reset_index()
wall_insulation_measures = measures_count[
measures_count["type"].isin(["cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation"])
]["id"].sum()
ventilation_measures = measures_count[
measures_count["type"].isin(["mechanical_ventilation"])
]["id"].sum()
roof_insulation_measures = measures_count[
measures_count["type"].isin(["loft_insulation", "flat_roof_insulation"])
]["id"].sum()
floor_insulation_measures = measures_count[
measures_count["type"].isin(["solid_floor_insulation", "suspended_floor_insulation"])
]["id"].sum()
windows = measures_count[
measures_count["type"].isin(["windows_glazing"])
]["id"].sum()
heating = measures_count[
measures_count["type"].isin(["heating"])
]["id"].sum()
heating_controls = measures_count[
measures_count["type"].isin(["heating_control"])
]["id"].sum()
solar = measures_count[
measures_count["type"].isin(["solar_pv"])
]["id"].sum()
other = measures_count[
~measures_count["type"].isin([
"cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation",
"loft_insulation", "flat_roof_insulation", "solid_floor_insulation",
"suspended_floor_insulation", "windows_glazing", "heating", "heating_control", "solar_pv",
"mechanical_ventilation"
])
]["id"].sum()
z = recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3_sample)]
@ -305,11 +546,54 @@ def scenario_2():
recommendations_summary["uprn"].astype(str).isin(archetype_1["uprn"].values)
]
arch_1_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_1["uprn"].values)
]
arch_1_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_1_recommendation_min = recommendations_arch_1_summary.min()
arch_1_recommendation_max = recommendations_arch_1_summary.max()
arch_1_recommendation_means = recommendations_arch_1_summary.mean()
arch_1_totals = recommendations_arch_1_summary.sum()
annual_total_co2 = recommendations_arch_1_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_1_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_1_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_1["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_1_recommendation_means['total_cost'], 2)}: "
f"{arch_1_recommendation_min['total_cost']} - {arch_1_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_1_recommendation_means['total_sap_points'], 2)}: "
f"{arch_1_recommendation_min['total_sap_points']} - {arch_1_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_1_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_1_recommendation_min['adjusted_heat_demand']} - "
f"{arch_1_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_1_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_1_recommendation_min['energy_percent_change']} - "
f"{arch_1_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_1_recommendation_means['total_carbon'], 2)}: "
f"{arch_1_recommendation_min['total_carbon']} - {arch_1_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_1_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_1_recommendation_min['carbon_percent_change']} - "
f"{arch_1_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_1_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_1_recommendation_min['total_bill_savings']} - "
f"{arch_1_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_1_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_1_recommendation_min['bills_percent_change']} - "
f"{arch_1_recommendation_max['bills_percent_change']}")
########################
# Archetype 2
########################
@ -318,11 +602,53 @@ def scenario_2():
recommendations_summary["uprn"].astype(str).isin(archetype_2["uprn"].values)
]
arch_2_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_2["uprn"].values)
]
arch_2_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_2_recommendation_min = recommendations_arch_2_summary.min()
arch_2_recommendation_max = recommendations_arch_2_summary.max()
arch_2_recommendation_means = recommendations_arch_2_summary.mean().round(2)
total_cost = recommendations_arch_2_summary["total_cost"].sum()
annual_total_co2 = recommendations_arch_2_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_2_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_2_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_2["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_2_recommendation_means['total_cost'], 2)}: "
f"{arch_2_recommendation_min['total_cost']} - {arch_2_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_2_recommendation_means['total_sap_points'], 2)}: "
f"{arch_2_recommendation_min['total_sap_points']} - {arch_2_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_2_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_2_recommendation_min['adjusted_heat_demand']} - "
f"{arch_2_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_2_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_2_recommendation_min['energy_percent_change']} - "
f"{arch_2_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_2_recommendation_means['total_carbon'], 2)}: "
f"{arch_2_recommendation_min['total_carbon']} - {arch_2_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_2_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_2_recommendation_min['carbon_percent_change']} - "
f"{arch_2_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_2_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_2_recommendation_min['total_bill_savings']} - "
f"{arch_2_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_2_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_2_recommendation_min['bills_percent_change']} - "
f"{arch_2_recommendation_max['bills_percent_change']}")
########################
# Archetype 3
########################
@ -331,11 +657,53 @@ def scenario_2():
recommendations_summary["uprn"].astype(str).isin(archetype_3["uprn"].values)
]
arch_3_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_3["uprn"].values)
]
arch_3_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_3_recommendation_min = recommendations_arch_3_summary.min()
arch_3_recommendation_max = recommendations_arch_3_summary.max()
arch_3_recommendation_means = recommendations_arch_3_summary.mean()
total_cost = recommendations_arch_3_summary["total_cost"].sum()
annual_total_co2 = recommendations_arch_3_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_3_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_3_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_3_recommendation_means['total_cost'], 2)}: "
f"{arch_3_recommendation_min['total_cost']} - {arch_3_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_3_recommendation_means['total_sap_points'], 2)}: "
f"{arch_3_recommendation_min['total_sap_points']} - {arch_3_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_3_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_3_recommendation_min['adjusted_heat_demand']} - "
f"{arch_3_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_3_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_3_recommendation_min['energy_percent_change']} - "
f"{arch_3_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_3_recommendation_means['total_carbon'], 2)}: "
f"{arch_3_recommendation_min['total_carbon']} - {arch_3_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_3_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_3_recommendation_min['carbon_percent_change']} - "
f"{arch_3_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_3_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_3_recommendation_min['total_bill_savings']} - "
f"{arch_3_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_3_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_3_recommendation_min['bills_percent_change']} - "
f"{arch_3_recommendation_max['bills_percent_change']}")
########################
# Archetype 4
########################
@ -344,11 +712,49 @@ def scenario_2():
recommendations_summary["uprn"].astype(str).isin(archetype_4["uprn"].values)
]
arch_4_property_details = property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
]
arch_4_property_details["co2_emissions"].sum() / property_details_df["co2_emissions"].sum()
# Take the mean, median and maximum of each value
arch_4_recommendation_min = recommendations_arch_4_summary.min()
arch_4_recommendation_max = recommendations_arch_4_summary.max()
arch_4_recommendation_means = recommendations_arch_4_summary.mean()
property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
]["total_floor_area"].mean()
total_cost = recommendations_arch_4_summary["total_cost"].sum()
annual_total_co2 = recommendations_arch_4_summary["total_carbon"].sum()
annual_total_bills = recommendations_arch_4_summary["total_bill_savings"].sum()
annual_total_energy_savings = recommendations_arch_4_summary["adjusted_heat_demand"].sum()
archetype_measures = \
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_4["uprn"].values)].groupby("type")[
"id"].count().reset_index()
cost_text = (f"{round(arch_4_recommendation_means['total_cost'], 2)}: "
f"{arch_4_recommendation_min['total_cost']} - {arch_4_recommendation_max['total_cost']}")
sap_text = (f"{round(arch_4_recommendation_means['total_sap_points'], 2)}: "
f"{arch_4_recommendation_min['total_sap_points']} - {arch_4_recommendation_max['total_sap_points']}")
energy_text = (f"{round(arch_4_recommendation_means['adjusted_heat_demand'], 2)}: "
f"{arch_4_recommendation_min['adjusted_heat_demand']} - "
f"{arch_4_recommendation_max['adjusted_heat_demand']}")
energy_percent_text = (f"{round(arch_4_recommendation_means['energy_percent_change'], 2)}: "
f"{arch_4_recommendation_min['energy_percent_change']} - "
f"{arch_4_recommendation_max['energy_percent_change']}")
carbon_text = (f"{round(arch_4_recommendation_means['total_carbon'], 2)}: "
f"{arch_4_recommendation_min['total_carbon']} - {arch_4_recommendation_max['total_carbon']}")
carbon_percent_text = (f"{round(arch_4_recommendation_means['carbon_percent_change'], 2)}: "
f"{arch_4_recommendation_min['carbon_percent_change']} - "
f"{arch_4_recommendation_max['carbon_percent_change']}")
bill_text = (f"{round(arch_4_recommendation_means['total_bill_savings'], 2)}: "
f"{arch_4_recommendation_min['total_bill_savings']} - "
f"{arch_4_recommendation_max['total_bill_savings']}")
bill_percent_text = (f"{round(arch_4_recommendation_means['bills_percent_change'], 2)}: "
f"{arch_4_recommendation_min['bills_percent_change']} - "
f"{arch_4_recommendation_max['bills_percent_change']}")

15
recommendations/HeatingRecommender.py
View file

@ -186,9 +186,18 @@ class HeatingRecommender:
# This upgrade will only take the heating system to average energy efficiency
heating_simulation_config["mainheat_energy_eff_ending"] = "Average"
# If the property is off-gas and has no heating system in place, the number of heated rooms will actually
# be 0, so we use the number of rooms as the figure
number_heated_rooms = (
self.property.data["number-heated-rooms"] if self.property.data["number-heated-rooms"] > 0
else (
self.property.number_of_rooms - 1 if self.property.number_of_rooms > 1 else
self.property.number_of_rooms
)
)
# Upgrade to electric storage heaters
costs = self.costs.high_heat_electric_storage_heaters(
number_heated_rooms=self.property.data["number-heated-rooms"]
number_heated_rooms=number_heated_rooms
)
description = "Install high heat retention electric storage heaters"
@ -268,9 +277,9 @@ class HeatingRecommender:
] and self.property.data["mains-gas-flag"]
is_combi = hotwater_from_mains or access_to_mains_no_system
if is_combi:
description = "Upgrade to a low carbon combi boiler"
description = "Upgrade to a new combi boiler"
else:
description = "Upgrade to a low carbon boiler"
description = "Upgrade to a new boiler"
simulation_config = {"mainheat_energy_eff_ending": "Good"}
if access_to_mains_no_system: