mirror of
https://github.com/Hestia-Homes/Model.git
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cleaning setting of kwh and energy bills
This commit is contained in:
Khalim Conn-Kowlessar
parent
6ec7995ac3
commit
2cb5308711
5 changed files with 236 additions and 223 deletions
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@ -30,6 +30,7 @@ from backend.app.plan.utils import get_cleaned
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from backend.app.utils import epc_to_sap_lower_bound, sap_to_epc
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from backend.app.utils import epc_to_sap_lower_bound, sap_to_epc
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from backend.ml_models.api import ModelApi
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from backend.ml_models.api import ModelApi
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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from backend.Property import Property
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from backend.Property import Property
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from backend.apis.GoogleSolarApi import GoogleSolarApi
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from backend.apis.GoogleSolarApi import GoogleSolarApi
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@ -722,228 +723,12 @@ async def trigger_plan(body: PlanTriggerRequest):
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property_recommendations = recommendations[property_id]
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property_recommendations = recommendations[property_id]
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property_instance = [p for p in input_properties if p.id == property_id][0]
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property_instance = [p for p in input_properties if p.id == property_id][0]
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kwh_impact_table = kwh_simulation_predictions["heating_kwh_predictions"][
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property_current_energy_bill = Recommendations.calculate_recommendation_tenant_savings(
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kwh_simulation_predictions["heating_kwh_predictions"]["property_id"] == str(property_id)
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property_instance=property_instance,
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].merge(
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kwh_simulation_predictions=kwh_simulation_predictions,
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kwh_simulation_predictions["hotwater_kwh_predictions"].drop(
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property_recommendations=property_recommendations
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columns=["property_id", "recommendation_id", "phase"]
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),
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how="inner",
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on="id",
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suffixes=("_heating", "_hotwater")
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).reset_index(drop=True)
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# We adjust this table with the kwh estimates for low energy lighting kwh values, and solar kwh estimates
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led_recommendation = pd.DataFrame([
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{
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"phase": r["phase"],
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"recommendation_id": r["recommendation_id"],
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"lighting_kwh_savings": r["kwh_savings"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
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} for recs in property_recommendations for r in recs if r["type"] == "low_energy_lighting"
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], columns=["phase", "recommendation_id", "lighting_kwh_savings"])
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solar_recommendations = pd.DataFrame([
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{
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"phase": r["phase"],
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"recommendation_id": r["recommendation_id"],
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"solar_kwh_savings": r["initial_ac_kwh_per_year"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
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} for recs in property_recommendations for r in recs if r["type"] == "solar_pv"
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], columns=["phase", "recommendation_id", "solar_kwh_savings"])
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# merge them on
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kwh_impact_table = kwh_impact_table.merge(
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led_recommendation, how="left", on=["phase", "recommendation_id"]
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).merge(
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solar_recommendations, how="left", on=["phase", "recommendation_id"]
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)
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property_kwh = property_instance.energy_consumption_estimates["unadjusted"]
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starting_dummy_id_value = -9999
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kwh_impact_table = pd.concat(
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[
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pd.DataFrame(
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[
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{
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"id": starting_dummy_id_value,
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"phase": starting_dummy_id_value,
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"recommendation_id": starting_dummy_id_value,
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"predictions_heating": property_kwh["heating"],
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"predictions_hotwater": property_kwh["hot_water"],
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}
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]
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),
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kwh_impact_table
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]
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).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
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for i in range(0, len(kwh_impact_table)):
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current_phase = kwh_impact_table.loc[i, 'phase']
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previous_phase_id = (current_phase - 1) if (current_phase > 0) else -9999
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previous_phase = kwh_impact_table[kwh_impact_table['phase'] == previous_phase_id]
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if not previous_phase.empty:
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for col in ["predictions_heating", "predictions_hotwater"]:
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if kwh_impact_table.loc[i, col] > previous_phase[col].max():
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kwh_impact_table.loc[i, col] = previous_phase[col].max()
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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# We adjust the predictions with the UCL model
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for k in ["heating", "hotwater"]:
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kwh_impact_table[f"adjusted_{k}"] = kwh_impact_table[f"predictions_{k}"].apply(
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lambda x: AnnualBillSavings.adjust_energy_to_metered(
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epc_energy=x, current_epc_rating=property_instance.data["current-energy-rating"]
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)
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)
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ASHP_COP = 3
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descriptions_to_fuel_types = {
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"Air source heat pump, radiators, electric": {"fuel": "Electricity", "cop": ASHP_COP},
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"Boiler and radiators, mains gas": {"fuel": 'Natural Gas', "cop": 0.9},
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'Electric storage heaters': {"fuel": 'Electricity', "cop": 1},
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"Electric immersion, off-peak": {"fuel": 'Electricity', "cop": 1},
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"Electric storage heaters, radiators": {"fuel": 'Electricity', "cop": 1},
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"Room heaters, electric": {"fuel": 'Electricity', "cop": 1},
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"Electric immersion, standard tariff": {"fuel": 'Electricity', "cop": 1},
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"Portable electric heaters assumed for most rooms": {"fuel": 'Electricity', "cop": 1},
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}
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def map_descriptions_to_fuel(heating_description, hotwater_description):
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mapped = descriptions_to_fuel_types[heating_description]
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heating_fuel = mapped["fuel"]
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if hotwater_description == "From main system":
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return {
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"heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel,
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"heating_cop": mapped["cop"], "hotwater_cop": mapped["cop"]
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}
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mapped_hotwater = descriptions_to_fuel_types[hotwater_description]
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return {
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"heating_fuel_type": heating_fuel, "hotwater_fuel_type": mapped_hotwater["fuel"],
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"heating_cop": mapped["cop"], "hotwater_cop": mapped_hotwater["cop"]
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}
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# For heating system recommendations, this could result in a fuel type change so we reflect that
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fuel_mapping = pd.DataFrame([
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{
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"id": epc["id"],
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**map_descriptions_to_fuel(epc["mainheat-description"], epc["hotwater-description"])
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} for epc in property_instance.updated_simulation_epcs
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])
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for epc in property_instance.updated_simulation_epcs:
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map_descriptions_to_fuel(epc["mainheat-description"], epc["hotwater-description"])
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fuel_mapping = pd.concat(
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[
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pd.DataFrame(
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[
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{
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"id": starting_dummy_id_value,
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**map_descriptions_to_fuel(
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property_instance.data["mainheat-description"],
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property_instance.data["hotwater-description"]
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)
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}
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]
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),
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fuel_mapping
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]
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)
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kwh_impact_table = kwh_impact_table.merge(
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fuel_mapping, how="left", on="id"
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).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
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kwh_impact_table["heating_fuel_type"] = np.where(
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kwh_impact_table["id"] == starting_dummy_id_value,
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property_instance.heating_energy_source,
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kwh_impact_table["heating_fuel_type"]
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)
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kwh_impact_table["hotwater_fuel_type"] = np.where(
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kwh_impact_table["id"] == starting_dummy_id_value,
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property_instance.hot_water_energy_source,
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kwh_impact_table["hotwater_fuel_type"]
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)
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def calculate_recommendation_fuel_cost(kwh, fuel, cop):
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if fuel == "Electricity":
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return (kwh / cop) * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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if fuel == "Natural Gas":
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return (kwh / cop) * AnnualBillSavings.GAS_PRICE_CAP
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# We now calculate the fuel cost
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for k in ["heating", "hotwater"]:
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kwh_impact_table[f"{k}_cost"] = kwh_impact_table.apply(
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lambda x: calculate_recommendation_fuel_cost(
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x[f"adjusted_{k}"], x[f"{k}_fuel_type"], x[f"{k}_cop"]
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), axis=1
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)
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# TODO: The impact of remapping EPC is huge!
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# We now deduce if any of the recommendations result in a change of fuel type
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for recs in property_recommendations:
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for rec in recs:
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if rec["type"] == "mechanical_ventilation":
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continue
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rec_impact = kwh_impact_table[kwh_impact_table["recommendation_id"] == rec["recommendation_id"]]
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prevous_phase_id = (rec["phase"] - 1) if (rec["phase"] > 0) else starting_dummy_id_value
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previous_phase_impact = kwh_impact_table[kwh_impact_table["phase"] == prevous_phase_id]
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if rec["type"] == "solar_pv":
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rec["kwh_savings"] = rec_impact["solar_kwh_savings"].values[0]
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rec["energy_cost_savings"] = (
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rec_impact["solar_kwh_savings"].values[0] * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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)
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continue
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heating_kwh_savings = (
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previous_phase_impact["adjusted_heating"].mean() - rec_impact["adjusted_heating"].values[0]
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)
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heating_cost_savings = (
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previous_phase_impact["heating_cost"].mean() - rec_impact["heating_cost"].values[0]
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)
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hotwater_kwh_savings = (
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previous_phase_impact["adjusted_hotwater"].mean() - rec_impact["adjusted_hotwater"].values[0]
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)
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hotwater_host = (
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previous_phase_impact["hotwater_cost"].mean() - rec_impact["hotwater_cost"].values[0]
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)
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total_kwh_savings = heating_kwh_savings + hotwater_kwh_savings
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energy_cost_savings = heating_cost_savings + hotwater_host
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if rec["type"] == "lighting":
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# In this case, we should probably just SKIP but check when we have one!
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raise Exception("Implement me 3")
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rec["kwh_savings"] = total_kwh_savings
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rec["energy_cost_savings"] = energy_cost_savings
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# Finally, we set the current energy bill
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starting_figures = kwh_impact_table[kwh_impact_table["id"] == starting_dummy_id_value].squeeze()
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gas_standing_charge = 0
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if (
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(starting_figures["heating_fuel_type"] == "Natural Gas") or
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(starting_figures["hotwater_fuel_type"] == "Natural Gas")
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):
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gas_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_GAS * 365
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electricity_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_ELECTRICITY * 365
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property_instance.current_energy_bill = (
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starting_figures["heating_cost"].values[0] +
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starting_figures["hotwater_cost"].values[0] +
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property_instance.energy_cost_estimates["unadjusted"]["lighting"] +
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property_instance.energy_cost_estimates["unadjusted"]["appliances"] +
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gas_standing_charge + electricity_standing_charge
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)
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)
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property_instance.current_energy_bill = property_current_energy_bill
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# Insert the predictions into the recommendations and run the optimiser
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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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# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
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@ -1284,7 +1069,7 @@ async def build_mds(body: MdsRequest):
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recommendations = {}
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recommendations = {}
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for p in tqdm(input_properties):
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for p in tqdm(input_properties):
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p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
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p.set_features(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
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mds = Mds(property_instance=p, materials=materials, optimise_measures=optimise_measures)
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mds = Mds(property_instance=p, materials=materials, optimise_measures=optimise_measures)
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mds_recommendations, property_representative_recommendations, errors = mds.build()
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mds_recommendations, property_representative_recommendations, errors = mds.build()
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@ -199,3 +199,11 @@ class AnnualBillSavings:
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return current_epc_rating
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return current_epc_rating
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return cls.EPC_BANDS[expected_index - 1]
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return cls.EPC_BANDS[expected_index - 1]
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@classmethod
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def calculate_recommendation_fuel_cost(cls, kwh, fuel, cop):
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if fuel == "Electricity":
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return (kwh / cop) * cls.ELECTRICITY_PRICE_CAP
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if fuel == "Natural Gas":
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return (kwh / cop) * cls.GAS_PRICE_CAP
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@ -134,7 +134,7 @@ def app():
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for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
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for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
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try:
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try:
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# Skip the first 50
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# Skip the first 50
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if i < 40:
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if i < 200:
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continue
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continue
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data = pd.read_csv(directory / "certificates.csv", low_memory=False)
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data = pd.read_csv(directory / "certificates.csv", low_memory=False)
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@ -17,6 +17,8 @@ def app():
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cleaned = msgpack.unpackb(cleaned, raw=False)
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cleaned = msgpack.unpackb(cleaned, raw=False)
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# If there is any problematic data, it could be:
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# s3://retrofit-datalake-dev/energy_consumption_data/2024-08-10 18:48:06.866647.pkl
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kwh_data_client = KwhData(bucket="retrofit-datalake-dev")
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kwh_data_client = KwhData(bucket="retrofit-datalake-dev")
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kwh_data_client.combine()
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kwh_data_client.combine()
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kwh_data_client.transform(data=kwh_data_client.data, cleaned=cleaned, save=True)
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kwh_data_client.transform(data=kwh_data_client.data, cleaned=cleaned, save=True)
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@ -17,6 +17,19 @@ from recommendations.SecondaryHeating import SecondaryHeating
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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from backend.apis.GoogleSolarApi import GoogleSolarApi
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from backend.apis.GoogleSolarApi import GoogleSolarApi
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ASHP_COP = 3
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DESCRIPTIONS_TO_FUEL_TYPES = {
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"Air source heat pump, radiators, electric": {"fuel": "Electricity", "cop": ASHP_COP},
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"Boiler and radiators, mains gas": {"fuel": 'Natural Gas', "cop": 0.9},
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'Electric storage heaters': {"fuel": 'Electricity', "cop": 1},
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"Electric immersion, off-peak": {"fuel": 'Electricity', "cop": 1},
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"Electric storage heaters, radiators": {"fuel": 'Electricity', "cop": 1},
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"Room heaters, electric": {"fuel": 'Electricity', "cop": 1},
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"Electric immersion, standard tariff": {"fuel": 'Electricity', "cop": 1},
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"Portable electric heaters assumed for most rooms": {"fuel": 'Electricity', "cop": 1},
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}
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STARTING_DUMMY_ID_VALUE = -9999
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class Recommendations:
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class Recommendations:
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"""
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"""
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@ -497,3 +510,208 @@ class Recommendations:
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)
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)
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return property_recommendations, impact_summary
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return property_recommendations, impact_summary
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@staticmethod
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def map_descriptions_to_fuel(heating_description, hotwater_description):
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mapped = DESCRIPTIONS_TO_FUEL_TYPES[heating_description]
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heating_fuel = mapped["fuel"]
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if hotwater_description == "From main system":
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return {
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|
"heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel,
|
||||||
|
"heating_cop": mapped["cop"], "hotwater_cop": mapped["cop"]
|
||||||
|
}
|
||||||
|
|
||||||
|
mapped_hotwater = DESCRIPTIONS_TO_FUEL_TYPES[hotwater_description]
|
||||||
|
|
||||||
|
return {
|
||||||
|
"heating_fuel_type": heating_fuel, "hotwater_fuel_type": mapped_hotwater["fuel"],
|
||||||
|
"heating_cop": mapped["cop"], "hotwater_cop": mapped_hotwater["cop"]
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def calculate_recommendation_tenant_savings(
|
||||||
|
cls, property_instance, kwh_simulation_predictions, property_recommendations
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
This method inserts the kwh savings and the bill savings that the customer will make from the recommendations
|
||||||
|
based on the predictions from the ML model
|
||||||
|
:param property_instance: Instance of the Property class, for the home associated to property_id
|
||||||
|
:param kwh_simulation_predictions: dictionary of predictions from the model apis
|
||||||
|
:param property_recommendations: dictionary of recommendations for the property
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
|
||||||
|
kwh_impact_table = kwh_simulation_predictions["heating_kwh_predictions"][
|
||||||
|
kwh_simulation_predictions["heating_kwh_predictions"]["property_id"] == str(property_instance.id)
|
||||||
|
].merge(
|
||||||
|
kwh_simulation_predictions["hotwater_kwh_predictions"].drop(
|
||||||
|
columns=["property_id", "recommendation_id", "phase"]
|
||||||
|
),
|
||||||
|
how="inner",
|
||||||
|
on="id",
|
||||||
|
suffixes=("_heating", "_hotwater")
|
||||||
|
).reset_index(drop=True)
|
||||||
|
|
||||||
|
# We adjust this table with the kwh estimates for low energy lighting kwh values, and solar kwh estimates
|
||||||
|
led_recommendation = pd.DataFrame([
|
||||||
|
{
|
||||||
|
"phase": r["phase"],
|
||||||
|
"recommendation_id": r["recommendation_id"],
|
||||||
|
"lighting_kwh_savings": r["kwh_savings"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
|
||||||
|
} for recs in property_recommendations for r in recs if r["type"] == "low_energy_lighting"
|
||||||
|
], columns=["phase", "recommendation_id", "lighting_kwh_savings"])
|
||||||
|
solar_recommendations = pd.DataFrame([
|
||||||
|
{
|
||||||
|
"phase": r["phase"],
|
||||||
|
"recommendation_id": r["recommendation_id"],
|
||||||
|
"solar_kwh_savings": r["initial_ac_kwh_per_year"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
|
||||||
|
} for recs in property_recommendations for r in recs if r["type"] == "solar_pv"
|
||||||
|
], columns=["phase", "recommendation_id", "solar_kwh_savings"])
|
||||||
|
|
||||||
|
# merge them on
|
||||||
|
kwh_impact_table = kwh_impact_table.merge(
|
||||||
|
led_recommendation, how="left", on=["phase", "recommendation_id"]
|
||||||
|
).merge(
|
||||||
|
solar_recommendations, how="left", on=["phase", "recommendation_id"]
|
||||||
|
)
|
||||||
|
|
||||||
|
property_kwh = property_instance.energy_consumption_estimates["unadjusted"]
|
||||||
|
|
||||||
|
kwh_impact_table = pd.concat(
|
||||||
|
[
|
||||||
|
pd.DataFrame(
|
||||||
|
[
|
||||||
|
{
|
||||||
|
"id": STARTING_DUMMY_ID_VALUE,
|
||||||
|
"phase": STARTING_DUMMY_ID_VALUE,
|
||||||
|
"recommendation_id": STARTING_DUMMY_ID_VALUE,
|
||||||
|
"predictions_heating": property_kwh["heating"],
|
||||||
|
"predictions_hotwater": property_kwh["hot_water"],
|
||||||
|
}
|
||||||
|
]
|
||||||
|
),
|
||||||
|
kwh_impact_table
|
||||||
|
]
|
||||||
|
).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
|
||||||
|
|
||||||
|
for i in range(0, len(kwh_impact_table)):
|
||||||
|
current_phase = kwh_impact_table.loc[i, 'phase']
|
||||||
|
previous_phase_id = (current_phase - 1) if (current_phase > 0) else -9999
|
||||||
|
previous_phase = kwh_impact_table[kwh_impact_table['phase'] == previous_phase_id]
|
||||||
|
|
||||||
|
if not previous_phase.empty:
|
||||||
|
for col in ["predictions_heating", "predictions_hotwater"]:
|
||||||
|
if kwh_impact_table.loc[i, col] > previous_phase[col].max():
|
||||||
|
kwh_impact_table.loc[i, col] = previous_phase[col].max()
|
||||||
|
|
||||||
|
# For heating system recommendations, this could result in a fuel type change so we reflect that
|
||||||
|
fuel_mapping = pd.DataFrame([
|
||||||
|
{
|
||||||
|
"id": epc["id"],
|
||||||
|
**cls.map_descriptions_to_fuel(epc["mainheat-description"], epc["hotwater-description"])
|
||||||
|
} for epc in property_instance.updated_simulation_epcs
|
||||||
|
])
|
||||||
|
|
||||||
|
fuel_mapping = pd.concat(
|
||||||
|
[
|
||||||
|
pd.DataFrame(
|
||||||
|
[
|
||||||
|
{
|
||||||
|
"id": STARTING_DUMMY_ID_VALUE,
|
||||||
|
**cls.map_descriptions_to_fuel(
|
||||||
|
property_instance.data["mainheat-description"],
|
||||||
|
property_instance.data["hotwater-description"]
|
||||||
|
)
|
||||||
|
}
|
||||||
|
]
|
||||||
|
),
|
||||||
|
fuel_mapping
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
kwh_impact_table = kwh_impact_table.merge(
|
||||||
|
fuel_mapping, how="left", on="id"
|
||||||
|
).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
|
||||||
|
|
||||||
|
kwh_impact_table["heating_fuel_type"] = np.where(
|
||||||
|
kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
|
||||||
|
property_instance.heating_energy_source,
|
||||||
|
kwh_impact_table["heating_fuel_type"]
|
||||||
|
)
|
||||||
|
|
||||||
|
kwh_impact_table["hotwater_fuel_type"] = np.where(
|
||||||
|
kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
|
||||||
|
property_instance.hot_water_energy_source,
|
||||||
|
kwh_impact_table["hotwater_fuel_type"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# We now calculate the fuel cost
|
||||||
|
for k in ["heating", "hotwater"]:
|
||||||
|
kwh_impact_table[f"{k}_cost"] = kwh_impact_table.apply(
|
||||||
|
lambda x: AnnualBillSavings.calculate_recommendation_fuel_cost(
|
||||||
|
x[f"adjusted_{k}"], x[f"{k}_fuel_type"], x[f"{k}_cop"]
|
||||||
|
), axis=1
|
||||||
|
)
|
||||||
|
|
||||||
|
# We now deduce if any of the recommendations result in a change of fuel type
|
||||||
|
for recs in property_recommendations:
|
||||||
|
for rec in recs:
|
||||||
|
if rec["type"] == "mechanical_ventilation":
|
||||||
|
continue
|
||||||
|
|
||||||
|
rec_impact = kwh_impact_table[kwh_impact_table["recommendation_id"] == rec["recommendation_id"]]
|
||||||
|
prevous_phase_id = (rec["phase"] - 1) if (rec["phase"] > 0) else STARTING_DUMMY_ID_VALUE
|
||||||
|
previous_phase_impact = kwh_impact_table[kwh_impact_table["phase"] == prevous_phase_id]
|
||||||
|
|
||||||
|
if rec["type"] == "solar_pv":
|
||||||
|
rec["kwh_savings"] = rec_impact["solar_kwh_savings"].values[0]
|
||||||
|
rec["energy_cost_savings"] = (
|
||||||
|
rec_impact["solar_kwh_savings"].values[0] * AnnualBillSavings.ELECTRICITY_PRICE_CAP
|
||||||
|
)
|
||||||
|
continue
|
||||||
|
|
||||||
|
heating_kwh_savings = (
|
||||||
|
previous_phase_impact["adjusted_heating"].mean() - rec_impact["adjusted_heating"].values[0]
|
||||||
|
)
|
||||||
|
heating_cost_savings = (
|
||||||
|
previous_phase_impact["heating_cost"].mean() - rec_impact["heating_cost"].values[0]
|
||||||
|
)
|
||||||
|
|
||||||
|
hotwater_kwh_savings = (
|
||||||
|
previous_phase_impact["adjusted_hotwater"].mean() - rec_impact["adjusted_hotwater"].values[0]
|
||||||
|
)
|
||||||
|
hotwater_host = (
|
||||||
|
previous_phase_impact["hotwater_cost"].mean() - rec_impact["hotwater_cost"].values[0]
|
||||||
|
)
|
||||||
|
|
||||||
|
total_kwh_savings = heating_kwh_savings + hotwater_kwh_savings
|
||||||
|
energy_cost_savings = heating_cost_savings + hotwater_host
|
||||||
|
|
||||||
|
if rec["type"] == "lighting":
|
||||||
|
# In this case, we should probably just SKIP but check when we have one!
|
||||||
|
raise Exception("Implement me 3")
|
||||||
|
|
||||||
|
rec["kwh_savings"] = total_kwh_savings
|
||||||
|
rec["energy_cost_savings"] = energy_cost_savings
|
||||||
|
|
||||||
|
# Finally, we set the current energy bill
|
||||||
|
starting_figures = kwh_impact_table[kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE].squeeze()
|
||||||
|
gas_standing_charge = 0
|
||||||
|
if (
|
||||||
|
(starting_figures["heating_fuel_type"] == "Natural Gas") or
|
||||||
|
(starting_figures["hotwater_fuel_type"] == "Natural Gas")
|
||||||
|
):
|
||||||
|
gas_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_GAS * 365
|
||||||
|
|
||||||
|
electricity_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_ELECTRICITY * 365
|
||||||
|
|
||||||
|
current_energy_bill = (
|
||||||
|
starting_figures["heating_cost"].values[0] +
|
||||||
|
starting_figures["hotwater_cost"].values[0] +
|
||||||
|
property_instance.energy_cost_estimates["unadjusted"]["lighting"] +
|
||||||
|
property_instance.energy_cost_estimates["unadjusted"]["appliances"] +
|
||||||
|
gas_standing_charge + electricity_standing_charge
|
||||||
|
)
|
||||||
|
|
||||||
|
return current_energy_bill
|
||||||
|
|
|
||||||
Loading…
Add table
Reference in a new issue