mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
minor peabody output work
This commit is contained in:
Khalim Conn-Kowlessar
parent
3809066cbf
commit
bae3e13e21
3 changed files with 317 additions and 20 deletions
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@ -1,8 +1,10 @@
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import pandas as pd
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df = pd.read_excel(
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"/Users/khalimconn-kowlessar/Downloads/Parity Data 08012026.xlsx"
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/Parity Data "
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"08012026.xlsx"
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)
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df["wall_combined"] = df["Wall Construction"] + "+" + df["Wall Insulation"].fillna("Unknown Insulation")
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df['SAP Score'].mean()
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@ -18,4 +20,72 @@ df["SAP Band"].value_counts(normalize=True)
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z = df[df["SAP Band"] != df["Lodged EPC Band"]]
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agg = z.groupby(["Lodged EPC Band", "SAP Band"]).size().reset_index(name="count")
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zz = z[z["Lodged EPC Band"] == "A"]
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recommendations_epc_c = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/EPC C - no "
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"solid floor, ashp 3.0 - corrected.xlsx"
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)
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recommendations_epc_c["uprn"] = recommendations_epc_c["uprn"].astype(int).astype(str)
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combined = recommendations_epc_c.merge(
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df,
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left_on="uprn",
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right_on="UPRN",
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suffixes=("_rec", "_sal")
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)
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combined = combined[["uprn", "SAP Score", "current_sap_points", "walls", "wall_combined"]]
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combined[combined["SAP Score"] < 69]["current_epc_rating"].value_counts()
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combined[combined["SAP Score"] < 69]["SAP Band"].value_counts()
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combined[combined["SAP Score"] < 69].shape
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combined[combined["current_sap_points"] < 69]
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combined["SAP Band"].value_counts()
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# Our Cs
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combined_cs = combined[combined["SAP Score"] < 69]
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combined_cs["SAP Band"].value_counts()
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# Their C and below
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compare = recommendations_epc_c[recommendations_epc_c["current_sap_points"] < 69]
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packages = recommendations_epc_c[recommendations_epc_c["total_retrofit_cost"] > 0]
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packages["current_epc_rating"].value_counts()
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# TODO: 612 units
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23219 - 612
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errors = recommendations_epc_c[
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(recommendations_epc_c["current_sap_points"] >= 69) &
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(recommendations_epc_c["total_retrofit_cost"] > 0)
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]
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errors["total_retrofit_cost"].sum()
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below_epc_c = recommendations_epc_c[recommendations_epc_c["current_sap_points"] < 69]
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below_epc_c_compare = below_epc_c.merge(
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df,
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left_on="uprn",
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right_on="UPRN",
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suffixes=("_rec", "_sal")
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)
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eg1 = below_epc_c_compare[below_epc_c_compare["SAP Band"] == "C"].copy()
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eg1["wall_combined"].value_counts()
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eg1_counts = eg1.groupby(["walls", "wall_combined"]).size().reset_index(name="count")
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eg1_counts = eg1_counts.sort_values("count", ascending=False)
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externally_insulated = eg1[
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(eg1["wall_combined"] == "Solid Brick+External") &
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pd.isnull(eg1["internal_wall_insulation"])
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]
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externally_insulated[externally_insulated.index == 823]["uprn"]
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recommendations_epc_c[
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(recommendations_epc_c["current_sap_points"] < 69) &
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(recommendations_epc_c["current_sap_points"] > 68)
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].shape
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recommendations_epc_c[recommendations_epc_c["wall_combined"] == ""]
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@ -0,0 +1,236 @@
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import pandas as pd
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epc_c_recommendations = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/EPC C - no "
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"solid floor, ashp 3.0 - corrected.xlsx"
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)
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epc_b_recommendations = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/EPC B - no "
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"solid floor, ashp 3.0 - corrected.xlsx"
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)
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epc_c_movers = epc_b_recommendations[
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epc_b_recommendations["current_epc_rating"] == "Epc.C"
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]
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epc_c_movers["property_type"].value_counts()
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house_epc_c_movers = epc_c_movers[
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epc_c_movers["property_type"] == "House"
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]
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house_epc_c_movers_with_solar = house_epc_c_movers[
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~pd.isnull(house_epc_c_movers["solar_pv"]) | ~pd.isnull(house_epc_c_movers["solar_pv_with_battery"])
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]
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house_epc_c_movers_with_a_heatpump = house_epc_c_movers[
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~pd.isnull(house_epc_c_movers["air_source_heat_pump"])
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]
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flat_epc_c_movers = epc_c_movers[
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epc_c_movers["property_type"] == "Flat"
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]
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epc_c_recommendations["sap_points"].mean()
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epc_c_recommendations["sap_points"].mean()
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measure_cols = [
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"air_source_heat_pump",
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"boiler_upgrade",
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"cavity_wall_insulation",
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"double_glazing",
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"external_wall_insulation",
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"flat_roof_insulation",
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"high_heat_retention_storage_heaters",
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"internal_wall_insulation",
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"loft_insulation",
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"low_energy_lighting",
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"mechanical_ventilation",
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"room_roof_insulation",
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"roomstat_programmer_trvs",
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"sealing_open_fireplace",
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"secondary_glazing",
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"secondary_heating",
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"solar_pv",
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"solar_pv_with_battery",
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"suspended_floor_insulation",
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"time_temperature_zone_control",
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]
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epc_c_melted = (
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epc_c_recommendations
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.melt(
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id_vars=[c for c in epc_c_recommendations.columns if c not in measure_cols],
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value_vars=measure_cols,
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var_name="measure_type",
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value_name="value",
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)
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.dropna(subset=["value"])
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)
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epc_c_melted = epc_c_melted[epc_c_melted["value"] > 0]
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epc_c_measures = epc_c_melted["measure_type"].value_counts(normalize=True).to_frame().reset_index()
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epc_b_melted = (
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epc_b_recommendations
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.melt(
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id_vars=[c for c in epc_b_recommendations.columns if c not in measure_cols],
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value_vars=measure_cols,
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var_name="measure_type",
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value_name="value",
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)
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.dropna(subset=["value"])
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)
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epc_b_melted = epc_b_melted[epc_b_melted["value"] > 0]
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epc_b_measures = epc_b_melted["measure_type"].value_counts(normalize=True).to_frame().reset_index()
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measures_compared = epc_c_measures.merge(
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epc_b_measures,
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left_on="measure_type",
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right_on="measure_type",
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suffixes=("_epc_c", "_epc_b"),
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)
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epc_c_retrofits = epc_c_recommendations[
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epc_c_recommendations["total_retrofit_cost"] > 0
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]
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epc_b_retrofits = epc_b_recommendations[
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epc_b_recommendations["total_retrofit_cost"] > 0
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]
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epc_c_retrofits["sap_points"].mean()
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epc_b_retrofits["sap_points"].mean()
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properties_in_both = epc_c_retrofits.merge(epc_b_retrofits, on="uprn", suffixes=("_epc_c", "_epc_b"))
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properties_in_both["total_retrofit_cost_epc_c"].mean()
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properties_in_both["sap_points_epc_c"].mean()
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properties_in_both["total_retrofit_cost_epc_b"].mean()
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properties_in_both["sap_points_epc_b"].mean()
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# Solar PV savings - we need the amount of solar PV bill savings
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from sqlalchemy.orm import sessionmaker
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from backend.app.db.connection import db_engine
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from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations, RecommendationMaterials
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from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
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from collections import defaultdict
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PORTFOLIO_ID = 434 # Peabody
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SCENARIOS = [
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904,
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905
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]
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scenario_names = {
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904: "EPC C - no solid floor, ashp 3.0",
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905: "EPC B - no solid floor, ashp 3.0",
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}
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def get_data(portfolio_id, scenario_ids):
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session = sessionmaker(bind=db_engine)()
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session.begin()
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# --------------------
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# Properties
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# --------------------
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properties_query = session.query(
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PropertyModel,
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PropertyDetailsEpcModel
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).join(
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PropertyDetailsEpcModel,
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PropertyModel.id == PropertyDetailsEpcModel.property_id
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).filter(
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PropertyModel.portfolio_id == portfolio_id
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).all()
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properties_data = [
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{
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**{col.name: getattr(p.PropertyModel, col.name)
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for col in PropertyModel.__table__.columns},
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**{col.name: getattr(p.PropertyDetailsEpcModel, col.name)
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for col in PropertyDetailsEpcModel.__table__.columns},
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}
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for p in properties_query
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]
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# --------------------
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# Plans
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# --------------------
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plans_query = session.query(Plan).filter(
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Plan.scenario_id.in_(scenario_ids)
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).all()
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plans_data = [
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{col.name: getattr(plan, col.name) for col in Plan.__table__.columns}
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for plan in plans_query
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]
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plan_ids = [p["id"] for p in plans_data]
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# --------------------
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# Recommendations (NO materials yet)
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# --------------------
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recommendations_query = session.query(
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Recommendation,
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Plan.scenario_id
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).join(
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PlanRecommendations,
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Recommendation.id == PlanRecommendations.recommendation_id
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).join(
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Plan,
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Plan.id == PlanRecommendations.plan_id
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).filter(
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PlanRecommendations.plan_id.in_(plan_ids),
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Recommendation.default.is_(True),
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Recommendation.already_installed.is_(False)
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).all()
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recommendations_data = [
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{
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**{col.name: getattr(r.Recommendation, col.name)
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for col in Recommendation.__table__.columns},
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"scenario_id": r.scenario_id,
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"materials": [] # placeholder
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}
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for r in recommendations_query
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]
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recommendation_ids = [r["id"] for r in recommendations_data]
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# --------------------
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# Recommendation materials (SEPARATE QUERY)
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# --------------------
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materials_query = session.query(
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RecommendationMaterials
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).filter(
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RecommendationMaterials.recommendation_id.in_(recommendation_ids)
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).all()
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# Group materials by recommendation_id
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materials_by_recommendation = defaultdict(list)
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for m in materials_query:
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materials_by_recommendation[m.recommendation_id].append({
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"material_id": m.material_id,
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"depth": m.depth,
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"quantity": m.quantity,
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"quantity_unit": m.quantity_unit,
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"estimated_cost": m.estimated_cost,
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})
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# Attach materials safely (no filtering side effects)
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for r in recommendations_data:
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r["materials"] = materials_by_recommendation.get(r["id"], [])
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session.close()
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return properties_data, plans_data, recommendations_data
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properties_data, plans_data, recommendations_data = get_data(
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portfolio_id=PORTFOLIO_ID, scenario_ids=SCENARIOS
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)
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recommendations_df = pd.DataFrame(recommendations_data)
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solar_pv_recommendations = recommendations_df[recommendations_df["measure_type"] == "solar_pv"]
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average_savings = solar_pv_recommendations.groupby("scenario_id")["energy_cost_savings"].mean().reset_index()
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@ -14,22 +14,14 @@ from collections import defaultdict
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# PORTFOLIO_ID = 206
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# SCENARIOS = [389]
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PORTFOLIO_ID = 419 # Peabody
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PORTFOLIO_ID = 434 # Peabody
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SCENARIOS = [
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871, # EPC C - fabric first, no solid floor, ashp 3.0
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863, # EPC B, No EWI/IWI, No Solid Floor, ASHP 3.0 COP
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862, # EPC B - No solid floor, ASHP COP 3.0
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861, # EPC C, No EWI/IWI, No Solid Floor, ASHP 3.0 COP
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859, # EPC C - no solid floor, ashp 3.0
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885, # EPC B - fabric first, no solid floor, ashp 3.0
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904,
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905
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]
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scenario_names = {
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871: "EPC C, fabric first, no solid floor, ashp 3.0",
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863: "EPC B, No EWI IWI, No Solid Floor, ASHP 3.0 COP",
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862: "EPC B, No solid floor, ASHP COP 3.0",
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861: "EPC C, No EWI IWI, No Solid Floor, ASHP 3.0 COP",
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859: "EPC C, no solid floor, ashp 3.0",
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885: "EPC B, fabric first, no solid floor, ashp 3.0"
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904: "EPC C - no solid floor, ashp 3.0",
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905: "EPC B - no solid floor, ashp 3.0",
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}
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@ -88,7 +80,8 @@ def get_data(portfolio_id, scenario_ids):
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Plan.id == PlanRecommendations.plan_id
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).filter(
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PlanRecommendations.plan_id.in_(plan_ids),
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Recommendation.default.is_(True)
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Recommendation.default.is_(True),
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Recommendation.already_installed.is_(False)
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).all()
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recommendations_data = [
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@ -220,9 +213,7 @@ for scenario_id in SCENARIOS:
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df = properties_df[
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[
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"landlord_property_id", "property_id", "uprn", "address", "postcode", "property_type", "walls", "roof",
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"heating", "windows",
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"current_epc_rating",
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"current_sap_points", "total_floor_area", "number_of_rooms",
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"heating", "windows", "current_epc_rating", "current_sap_points", "total_floor_area", "number_of_rooms",
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]
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].merge(
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recommendations_measures_pivot, how="left", on="property_id"
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@ -240,7 +231,7 @@ for scenario_id in SCENARIOS:
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# Create excel to store to
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filename = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting "
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f"Project/{scenario_names[scenario_id]}.xlsx")
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f"Project/Final SAL/{scenario_names[scenario_id]} - corrected.xlsx")
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with pd.ExcelWriter(filename) as writer:
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df.to_excel(writer, sheet_name="properties", index=False)
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