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updated price cap figures
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Khalim Conn-Kowlessar
parent
80fc7c821e
commit
053218b3fd
4 changed files with 73 additions and 24 deletions
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@ -196,7 +196,6 @@ async def trigger_plan(body: PlanTriggerRequest):
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)
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model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
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# model_api.MODEL_PREFIXES = ['sap_change_predictions', 'carbon_change_predictions']
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all_predictions = {
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"sap_change_predictions": pd.DataFrame(),
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@ -219,9 +218,6 @@ async def trigger_plan(body: PlanTriggerRequest):
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for key, scored in predictions_dict.items():
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all_predictions[key] = pd.concat([all_predictions[key], scored])
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# TODO: TEMP
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# all_predictions["heat_demand_predictions"] = all_predictions["sap_change_predictions"].copy()
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# Insert the predictions into the recommendations and run the optimiser
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# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
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# possibility with heating system
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@ -10,13 +10,13 @@ class AnnualBillSavings:
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AVERAGE_ELECTRICITY_CONSUMPTION = 2700
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AVERAGE_GAS_CONSUMPTION = 11500
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# Latest price cap figures from Ofgem are for January 2024
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# https://www.ofgem.gov.uk/publications/changes-energy-price-cap-1-january-2024
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ELECTRICITY_PRICE_CAP = 0.29
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GAS_PRICE_CAP = 0.07
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# Latest price cap figures from Ofgem are for April 2024
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# https://www.ofgem.gov.uk/publications/new-energy-price-cap-level-april-june-2024-starts-today
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ELECTRICITY_PRICE_CAP = 0.245
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GAS_PRICE_CAP = 0.0604
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# This is a weighted mean of the price caps, using the consumption figures above as weights
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PRICE_FACTOR = 0.11183098591549295
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PRICE_FACTOR = 0.09549999999999999
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EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
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@ -35,20 +35,20 @@ def app():
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# 79% D, 19% E, 1% F, 0.2% G - it probably makes the most sense to focus on E and D properties
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epc_data["CURRENT_ENERGY_RATING"].value_counts(normalize=True)
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# For the purpose of the sample, take the properties have surveys done in the last 2 years
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# This gives us 1167 remaining properties
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two_years_ago = pd.Timestamp.now() - pd.DateOffset(days=int(2.5 * 365))
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epc_data = epc_data[epc_data["LODGEMENT_DATE"] >= two_years_ago]
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# For the purpose of the sample, take the properties have surveys done in the last 3 years
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# This gives us 1351 remaining properties
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three_years_ago = pd.Timestamp.now() - pd.DateOffset(days=int(3 * 365))
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epc_data = epc_data[epc_data["LODGEMENT_DATE"] >= three_years_ago]
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# Archetype 1: defined below:
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# 1) House
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# 2) Unfilled cavity
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# 3) A roof that could be insulated (flat or pitched with no more than 50mm insulation)
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# 4) EPC E
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# 12 properties
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# 4) EPC E or D
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# 24 properties
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archetype_1_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["House"]) &
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(epc_data["CURRENT_ENERGY_RATING"] == "E") &
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(epc_data["CURRENT_ENERGY_RATING"].isin(["D", "E"])) &
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epc_data["WALLS_DESCRIPTION"].isin(["Cavity wall, as built, no insulation (assumed)"]) &
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epc_data["ROOF_DESCRIPTION"].isin(
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[
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@ -69,10 +69,10 @@ def app():
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# 2) Unfilled cavity
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# 3) Another property above
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# 4) EPC E
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# 14 properties here
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# 57 properties here
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archetype_2_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["Flat"]) &
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(epc_data["CURRENT_ENERGY_RATING"] == "E") &
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(epc_data["CURRENT_ENERGY_RATING"].isin(["E", "D"])) &
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epc_data["WALLS_DESCRIPTION"].isin(["Cavity wall, as built, no insulation (assumed)"]) &
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epc_data["ROOF_DESCRIPTION"].isin(
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[
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@ -88,11 +88,18 @@ def app():
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# 2) Solid brick wall
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# 3) House
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# 4) Pitched roof with no insulation
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# Just 1 property (more expensive to retrofit)
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# Just 7 properties (more expensive to retrofit)
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archetype_3_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["House"]) &
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(epc_data["CURRENT_ENERGY_RATING"] == "F") &
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epc_data["ROOF_DESCRIPTION"].isin(["Pitched, no insulation"])
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(epc_data["CURRENT_ENERGY_RATING"].isin(["F", "G"])) &
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epc_data["ROOF_DESCRIPTION"].isin(
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[
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"Pitched, no insulation",
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"Pitched, limited insulation (assumed)",
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"Pitched, 100 mm loft insulation",
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"Pitched, no insulation (assumed)",
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]
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)
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]
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archetype_3_sample_asset_list = archetype_3_sample[["UPRN", "ADDRESS1", "POSTCODE"]].copy()
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archetype_3_sample_asset_list["ARCHETYPE"] = "Archetype 3"
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@ -101,15 +108,18 @@ def app():
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# 1) Maisonette
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# 2) Empty cavity
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# 3) EPC E
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# 14 properties here
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# 16 properties here
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archetype_4_sample = epc_data[
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epc_data["PROPERTY_TYPE"].isin(["Maisonette"]) &
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epc_data["WALLS_DESCRIPTION"].isin(["Cavity wall, as built, no insulation (assumed)"])
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epc_data["WALLS_DESCRIPTION"].isin(
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["Cavity wall, as built, no insulation (assumed)"]
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)
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]
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archetype_4_sample_asset_list = archetype_4_sample[["UPRN", "ADDRESS1", "POSTCODE"]].copy()
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archetype_4_sample_asset_list["ARCHETYPE"] = "Archetype 4"
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# 41 total properties
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# 104 total properties
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asset_list = pd.concat(
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[
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archetype_1_sample_asset_list,
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@ -38,7 +38,50 @@ def app():
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asset_list = read_csv_from_s3(
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"retrofit-plan-inputs-dev", f"{USER_ID}/{portfolio_id}/inputs.csv"
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)
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asset_list = pd.DataFrame(asset_list)
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# Get the properties for the portfolio
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properties = get_properties_with_default_recommendations(session, portfolio_id)
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properties_df = pd.DataFrame(properties)
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# We now pull the data for the property details
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property_details = get_property_details_by_portfolio_id(session, portfolio_id)
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property_details_df = pd.DataFrame(property_details)
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# Merge on uprn
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property_details_df = property_details_df.merge(
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properties_df[["uprn", "id"]].rename(columns={"id": "property_id"}),
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on="property_id"
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)
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plans = get_plan_by_portfolio_id(session, portfolio_id)
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plans_df = pd.DataFrame(plans)
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# Unnest the recommendations. Each recommendation is a list of dictionaries
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recommendations_exploded = properties_df["recommendations"].explode().tolist()
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recommendations_df = pd.DataFrame([r for r in recommendations_exploded if not pd.isnull(r)])
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# Add uprn on
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recommendations_df = recommendations_df.merge(
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properties_df[["uprn", "id"]].rename(columns={"id": "property_id"}),
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how="left",
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on="property_id"
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)
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# Summary information by each archetype
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archetype_1 = asset_list[asset_list["archetype"] == "Archetype 1"]
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recommendations_arch_1_summary = create_recommendations_summary(
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recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_1["uprn"].values)],
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properties_df[properties_df["uprn"].astype(str).isin(archetype_1["uprn"].values)],
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SAP_TARGET_1
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)
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# Take the mean, median and maximum of each value
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arch_1_recommendation_means = recommendations_arch_1_summary.mean()
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arch_1_property_details = property_details_df[
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property_details_df["uprn"].astype(str).isin(archetype_1["uprn"].values)
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]
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arch_1_property_details_means = arch_1_property_details.mean()
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arch_1_recommendation_means["total_bill_savings"] / arch_1_property_details_means["adjusted_energy_consumption"]
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