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peabody specific
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Khalim Conn-Kowlessar
parent
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commit
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4 changed files with 193 additions and 15 deletions
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@ -114,14 +114,16 @@ from backend.app.db.models.recommendations import Recommendation, Plan, PlanReco
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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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PORTFOLIO_ID = 435 # Peabody
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SCENARIOS = [
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904,
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905
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908,
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909,
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910,
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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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908: "EPC C - no solid floor, ashp 3.0",
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909: "EPC C - no solid floor, no EWI or IWI, ashp 3.0",
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910: "EPC B - no solid floor, no EWI, ashp 3.0"
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}
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@ -232,9 +234,58 @@ properties_data, plans_data, recommendations_data = get_data(
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recommendations_df = pd.DataFrame(recommendations_data)
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properties_df = pd.DataFrame(properties_data)
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plans_df = pd.DataFrame(plans_data)
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solar_pv_recommendations = recommendations_df[recommendations_df["measure_type"] == "solar_pv"]
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s_id = 910
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ps_w_a_plan = plans_df[plans_df["scenario_id"] == s_id].copy()
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# Take the newest by scenario id
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ps_w_a_plan = ps_w_a_plan.sort_values("created_at", ascending=False).drop_duplicates(
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subset=["property_id"]
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)
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z = ps_w_a_plan[
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ps_w_a_plan["cost_of_works"] > 0
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].copy()
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z2 = properties_df[properties_df["property_id"].isin(z["property_id"].values)]
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# '', 'hot_water_cost_current',
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# 'lighting_cost_current', 'appliances_cost_current',
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# 'gas_standing_charge', 'electricity_standing_charge'
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z2["total_bills"] = z2["heating_cost_current"] + z2["hot_water_cost_current"] + z2["lighting_cost_current"] + z2[
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"appliances_cost_current"
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] + z2["gas_standing_charge"] + z2["electricity_standing_charge"]
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from tqdm import tqdm
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# For a property ID, find a property where the no EWI/IWI approach is more expensive than the EWI approach
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pids = properties_df["property_id"].unique()
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for pid in tqdm(pids):
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if pid in [603272, 550550, 574493]:
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continue
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# get the plans
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property_plan = plans_df[plans_df["property_id"] == int(pid)]
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# Take the newest plan by scenario id
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property_plan = property_plan.sort_values("created_at", ascending=False).drop_duplicates(
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subset=["scenario_id"]
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)
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a = property_plan[property_plan["scenario_id"] == 909].squeeze() # no EWI/IWI
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b = property_plan[property_plan["scenario_id"] == 908].squeeze() # EWI
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if (a["cost_of_works"] > b["cost_of_works"]) and (
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a["post_epc_rating"].value == "C") and (b["cost_of_works"] > 5000):
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bah
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solar_pv_recommendations = recommendations_df[
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recommendations_df["measure_type"] == "solar_pv"
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]
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solid_wall_recommendation = recommendations_df[
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recommendations_df["scenario_id"].isin([908]) &
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recommendations_df["measure_type"].isin(["internal_wall_insulation"]) &
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recommendations_df["default"]
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]
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average_savings = solar_pv_recommendations.groupby("scenario_id")["energy_cost_savings"].mean().reset_index()
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# Add on scenarion names
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average_savings["scenario_name"] = average_savings["scenario_id"].map(scenario_names)
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# Check tenures
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initial_asset_data = pd.read_excel(
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@ -11,7 +11,6 @@ from etl.customers.cambridge.surveys import current_epc
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with db_session() as session:
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# We need installed measures, where the measure type is ewi or iwi
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installed_measures = session.query(InstalledMeasure).filter(
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InstalledMeasure.measure_type.in_(["cavity_wall_insulation"])
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).all()
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# Get the uprns
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installed_uprns = [x.uprn for x in installed_measures]
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@ -32,7 +31,7 @@ needing_retry = sal[sal["epc_os_uprn"].isin(installed_uprns)]
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# Store
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needing_retry.to_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final "
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"SAL/properties_needing_retry_20260115 - cavity wall insulation.xlsx",
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"SAL/properties_needing_retry_20260115 - all already installed.xlsx",
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sheet_name="Standardised Asset List",
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index=False
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)
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@ -0,0 +1,41 @@
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# get all properties that have an IWI recommendation
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import pandas as pd
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r1 = 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, no EWI, ashp 3.0 - 20250113 final.xlsx"
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)
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r2 = 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 - 20250113 final.xlsx"
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)
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r3 = 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, no EWI or IWI, ashp 3.0 - 20250113 final.xlsx"
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)
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s1 = r1[~pd.isnull(r1["internal_wall_insulation"])]
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s2 = r2[~pd.isnull(r2["internal_wall_insulation"])]
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# Combined uprns
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uprns = s1["uprn"].tolist() + s2["uprn"].tolist()
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uprns = list(set(uprns))
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# Create SAL of these uprns
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sal = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/20260113 - "
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"final asset list.xlsx",
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sheet_name="Standardised Asset List"
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)
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needing_retry = sal[sal["epc_os_uprn"].isin(uprns)]
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# Store
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needing_retry.to_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final "
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"SAL/properties_needing_retry_20260115 - internal wall insulation.xlsx",
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sheet_name="Standardised Asset List",
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index=False
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)
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@ -11,6 +11,7 @@ from backend.app.db.models.recommendations import Recommendation, Plan, PlanReco
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from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel, PropertyDetailsSpatial
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from backend.app.db.functions.materials_functions import get_materials
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from collections import defaultdict
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from sqlalchemy import func
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# PORTFOLIO_ID = 206
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# SCENARIOS = [389]
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@ -57,9 +58,44 @@ def get_data(portfolio_id, scenario_ids):
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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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latest_plans_subq = (
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session.query(
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Plan.scenario_id,
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Plan.property_id,
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func.max(Plan.created_at).label("latest_created_at")
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)
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.filter(Plan.scenario_id.in_(scenario_ids))
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.group_by(
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Plan.scenario_id,
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Plan.property_id
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)
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.subquery()
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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_query = (
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session.query(Plan)
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.join(
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latest_plans_subq,
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(Plan.scenario_id == latest_plans_subq.c.scenario_id) &
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(Plan.property_id == latest_plans_subq.c.property_id) &
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(Plan.created_at == latest_plans_subq.c.latest_created_at)
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)
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.all()
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)
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# plans_query = (
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# session.query(Plan)
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# .join(
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# latest_plans_subq,
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# (Plan.scenario_id == latest_plans_subq.c.scenario_id) &
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# (Plan.created_at == latest_plans_subq.c.latest_created_at)
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# )
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# .all()
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# )
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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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@ -73,7 +109,8 @@ def get_data(portfolio_id, scenario_ids):
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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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Plan.scenario_id,
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PlanRecommendations.plan_id
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).join(
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PlanRecommendations,
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Recommendation.id == PlanRecommendations.recommendation_id
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@ -216,6 +253,7 @@ for scenario_id in SCENARIOS:
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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", "current_epc_rating", "current_sap_points", "total_floor_area", "number_of_rooms",
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"id"
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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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@ -223,17 +261,42 @@ for scenario_id in SCENARIOS:
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post_install_sap, how="left", on="property_id"
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)
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df = df.drop(columns=["property_id"])
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# df = df.drop(columns=["property_id"])
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df["sap_points"] = df["sap_points"].fillna(0)
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df["predicted_post_works_sap"] = df["current_sap_points"] + df["sap_points"]
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df["predicted_post_works_sap"] = df["predicted_post_works_sap"].round()
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df["predicted_post_works_sap"] = df["predicted_post_works_sap"]
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df["predicted_post_works_epc"] = df["predicted_post_works_sap"].apply(lambda x: sap_to_epc(x))
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df["uprn"] = df["uprn"].astype(str)
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relevant_plans = plans_df[plans_df["scenario_id"] == scenario_id]
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df2 = df.merge(
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relevant_plans[["property_id", "post_sap_points", "post_epc_rating"]], how="left", on="property_id",
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suffixes=("", "_plan")
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)
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print(df2["predicted_post_works_epc"].value_counts())
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print(df2["post_epc_rating"].value_counts())
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z = df2[
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(df2["predicted_post_works_epc"] != "D") &
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(df2["post_epc_rating"].astype(str) == "Epc.D")
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]
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df2["predicted_post_works_epc"].value_counts()
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df2["post_epc_rating"].astype(str).value_counts()
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df2[df2["total_retrofit_cost"] > 0].shape
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getting_works = df[df["total_retrofit_cost"] > 0]
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getting_works["predicted_post_works_epc"].value_counts()
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32565 / getting_works.shape[0]
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df[df["predicted_post_works_sap"] == ""]
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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/Final SAL/{scenario_names[scenario_id]} - 20250113 final.xlsx")
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f"Project/Final SAL/scenarios/{scenario_names[scenario_id]} - 20250114 final.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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@ -388,3 +451,27 @@ asset_list.to_excel(
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condition_cost_comparison = asset_list[
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["condition_score", "decoration_sum_min ", "decoration_sum_max", "domna_condition_cost"]
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]
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# Testing
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plans_df.head()
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example = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final "
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"SAL/scenarios/EPC C - no solid floor, no EWI or IWI, ashp 3.0 - 20250114 final.xlsx"
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)
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plans_df2 = plans_df.merge(
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properties_df[["property_id", "landlord_property_id"]],
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left_on="property_id",
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right_on="property_id",
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how="left"
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)
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plans_df2 = plans_df2[plans_df2["scenario_id"] == 909]
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dupes = plans_df2[plans_df2["property_id"].duplicated()]
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# merge on plans
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example = example.merge(
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plans_df, how="left",
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)
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