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implemented recommendation level kwh and cost savings
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Khalim Conn-Kowlessar
parent
a0eabd5f09
commit
2efb2a4f3e
2 changed files with 160 additions and 22 deletions
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@ -183,6 +183,7 @@ class Property:
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self.recommendations_scoring_data = []
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self.simulation_epcs = {}
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self.updated_simulation_epcs = []
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# This additional condition data should change how we pass kwargs to this. We should no longer need to pass
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# kwargs to this class, but instead, we should pass the energy assessment condition data
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@ -454,6 +455,9 @@ class Property:
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)
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updated_simulation_epcs.append(sim_epc)
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# Now we havet this data inthe
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self.updated_simulation_epcs = updated_simulation_epcs
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return updated_simulation_epcs
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@staticmethod
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@ -671,29 +671,14 @@ async def trigger_plan(body: PlanTriggerRequest):
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# We use the impact_summary to update the simulation_epcs with the new SAP, heat demand, carbon, cost etc
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# at each phase
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property_scoring_epcs = property_instance.update_simulation_epcs(impact_summary)
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scoring_epcs.extend(property_scoring_epcs)
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property_instance.update_simulation_epcs(impact_summary)
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scoring_epcs.extend(property_instance.updated_simulation_epcs)
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recommendations[property_id] = recommendations_with_impact
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# We call the API with the scoring epcs
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scoring_epcs = pd.DataFrame(scoring_epcs)
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scoring_epcs = kwh_client.transform(data=scoring_epcs, cleaned=cleaned)
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# There should be no difference between index 9 and index 8, apart from photo-supply (other that sap, etc)
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a = scoring_epcs[scoring_epcs.index == 6]
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b = scoring_epcs[scoring_epcs.index == 11]
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difference = []
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for col in a.columns:
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if a[col].values[0] != b[col].values[0]:
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difference.append(
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{
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"col": col,
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"without_solar": a[col].values[0],
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"with_solar": b[col].values[0]
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}
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)
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difference = pd.DataFrame(difference)
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kwh_simulation_predictions = model_api.paginated_predictions(
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data=scoring_epcs,
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bucket=get_settings().DATA_BUCKET,
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@ -715,16 +700,42 @@ async def trigger_plan(body: PlanTriggerRequest):
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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": None,
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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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@ -732,7 +743,19 @@ async def trigger_plan(body: PlanTriggerRequest):
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),
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kwh_impact_table
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]
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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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@ -741,13 +764,124 @@ async def trigger_plan(body: PlanTriggerRequest):
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)
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)
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kwh_impact_table["heating_fuel"] = property_instance.heating_energy_source
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kwh_impact_table["hotwater_fuel"] = property_instance.hot_water_energy_source
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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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}
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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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raise NotImplementedError("Implement me")
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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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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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print(rec["description_simulation"])
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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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# TODO: Given the default recommendations, calcualte a total kwh and cost saving for the property!!!
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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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