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prepared the optimised output"
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Khalim Conn-Kowlessar
parent
5e8930f265
commit
9284d6cf76
2 changed files with 148 additions and 12 deletions
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@ -773,7 +773,7 @@ async def build_mds(body: MdsRequest):
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data = p.recommendations_scoring_data.copy()
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for d in data:
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d["id"] = d["id"] + _id
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d["id"] = d["id"] + "*" + _id
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recommendations_scoring_data.extend(data)
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@ -823,6 +823,142 @@ async def build_mds(body: MdsRequest):
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if p["uprn"]:
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p["uprn"] = str(int(float(p["uprn"])))
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import re
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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if optimise_measures:
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results = []
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for p in input_properties:
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package_comparison = []
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for _id in recommendations[p.id].keys():
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sap_prediction = all_predictions["sap_change_predictions"][
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(all_predictions["sap_change_predictions"]["property_id"] == str(p.id)) &
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(all_predictions["sap_change_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
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].copy().reset_index(drop=True)
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sap_prediction["row_id"] = sap_prediction.index
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heat_demand_prediction = all_predictions["heat_demand_predictions"][
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(all_predictions["heat_demand_predictions"]["property_id"] == str(p.id)) &
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(all_predictions["heat_demand_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
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].copy().reset_index(drop=True)
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heat_demand_prediction["row_id"] = heat_demand_prediction.index
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carbon_prediction = all_predictions["carbon_change_predictions"][
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(all_predictions["carbon_change_predictions"]["property_id"] == str(p.id)) &
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(all_predictions["carbon_change_predictions"]["recommendation_id"].str.contains(re.escape(_id)))
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].copy().reset_index(drop=True)
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carbon_prediction["row_id"] = carbon_prediction.index
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epc_target = body.goal_value
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sap_target = epc_to_sap_lower_bound(epc_target)
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# Define the measures
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sap_threshold_barrier = sap_prediction[sap_prediction["predictions"] >= sap_target]
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if sap_threshold_barrier.empty:
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raise NotImplementedError("FIX ME")
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sap_threshold_barrier = sap_threshold_barrier.head(1)
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sap_prediction = sap_prediction[
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sap_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
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]
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heat_demand_prediction = heat_demand_prediction[
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heat_demand_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
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]
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carbon_prediction = carbon_prediction[
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carbon_prediction["row_id"] <= sap_threshold_barrier["row_id"].values[0]
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]
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reverse_map = {v: k for k, v in Mds.format_map.items()}
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selected_measures = [
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reverse_map[x.split("-")[0]] for x in sap_prediction["recommendation_id"].values
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]
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selected_measure_ids = [x.split("*")[0] for x in sap_prediction["recommendation_id"].values]
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costs = [
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r["total"] for r in representative_recommendations[p.id][_id] if
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r["recommendation_id"] in selected_measure_ids
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]
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costs = sum(costs)
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sap_before = int(p.data["current-energy-efficiency"])
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sap_after = sap_prediction["predictions"].values[-1]
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epc_before = p.data["current-energy-rating"]
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epc_after = sap_to_epc(sap_after)
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heat_demand_before = p.data["energy-consumption-current"]
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heat_demand_after = heat_demand_prediction["predictions"].values[-1]
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carbon_before = p.data["co2-emissions-current"]
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carbon_after = carbon_prediction["predictions"].values[-1]
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current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
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epc_energy_consumption=heat_demand_before * p.floor_area,
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current_epc_rating=epc_before,
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)
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expected_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
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epc_energy_consumption=heat_demand_after * p.floor_area,
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current_epc_rating=epc_before,
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)
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current_energy_bill = AnnualBillSavings.calculate_annual_bill(current_adjusted_energy)
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expected_energy_bill = AnnualBillSavings.calculate_annual_bill(expected_adjusted_energy)
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bill_savings = current_energy_bill - expected_energy_bill
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energy_savings = current_adjusted_energy - expected_adjusted_energy
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package_comparison.append(
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{
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"id": _id,
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"cost": costs,
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"measures": selected_measures,
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"sap_before": sap_before,
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"sap_after": sap_after,
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"epc_before": epc_before,
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"epc_after": epc_after,
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"heat_demand_before": heat_demand_before,
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"heat_demand_after": heat_demand_after,
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"carbon_before": carbon_before,
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"carbon_after": carbon_after,
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"bill_savings": bill_savings,
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"energy_savings": energy_savings,
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}
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)
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package_comparison = pd.DataFrame(package_comparison)
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# Find the smallest cost package
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package_comparison = package_comparison.sort_values("cost")
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package_comparison = package_comparison.head(1).to_dict("records")[0]
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config = [c for c in plan_input if c["uprn"] == str(p.uprn)]
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if not config:
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config = {"address": None, "postcode": None}
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else:
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config = config[0]
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results.append({
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"config_address": config["address"],
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"config_postcode": config["postcode"],
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"address": p.address,
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"postcode": p.postcode,
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"measures": package_comparison["measures"],
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"year_of_epc": p.data['lodgement-date'],
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"sap_before": package_comparison["sap_before"],
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"sap_after": package_comparison["sap_after"],
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"epc_before": package_comparison["epc_before"],
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"epc_after": package_comparison["epc_after"],
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"heat_demand_before": package_comparison["heat_demand_before"],
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"heat_demand_after": package_comparison["heat_demand_after"],
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"carbon_before": package_comparison["carbon_before"],
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"carbon_after": package_comparison["carbon_after"],
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"bill_savings": package_comparison["bill_savings"],
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"energy_savings": package_comparison["energy_savings"],
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})
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results = pd.DataFrame(results)
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results = []
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for p in input_properties:
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measures = p.measures
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@ -22,6 +22,16 @@ class Mds:
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Handles the contruction of the MDS report
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"""
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format_map = {
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"external_wall_insulation": "EWI (Trad Const)",
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"internal_wall_insualtion": "IWI",
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"cavity_wall_insulation": "CWI",
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"loft_insulation": "LI",
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"air_source_heat_pump": "ASHP Htg",
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"high_heat_retention_storage_heaters": "High Heat Retention Storage Heaters",
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"solar_pv": "Solar PV",
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}
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def __init__(self, property_instance: Property, materials, optimise_measures: bool = False):
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self.property_instance = property_instance
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@ -55,16 +65,6 @@ class Mds:
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'solar_pv'
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]
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format_map = {
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"external_wall_insulation": "EWI (Trad Const)",
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"internal_wall_insualtion": "IWI",
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"cavity_wall_insulation": "CWI",
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"loft_insulation": "LI",
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"air_source_heat_pump": "ASHP Htg",
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"high_heat_retention_storage_heaters": "High Heat Retention Storage Heaters",
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"solar_pv": "Solar PV",
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}
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# Check if our measures are within the ones we've handled
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new = [m for m in measures if m not in all_considered_measures]
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if new:
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@ -159,7 +159,7 @@ class Mds:
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pruned_measures_formatted = []
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for pm in pruned_measures:
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pruned_measures_formatted.append({pm: format_map[pm]})
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pruned_measures_formatted.append({pm: self.format_map[pm]})
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pruned_combinations.append(pruned_measures_formatted)
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