completed the current energy bill estimate

backend/Property.py

@@ -112,6 +112,8 @@ class Property:
         self.wall_type = None
         self.floor_type = None
 
+        self.energy_cost_estimates = {}
+
         self.energy = {
             "primary_energy_consumption": epc_record.get("energy_consumption_current"),
             "co2_emissions": epc_record.get("co2_emissions_current"),
@@ -612,26 +614,63 @@ class Property:
         for col in ["heating_kwh", "hot_water_kwh"]:
             scoring_df[col] = None
 
+        # We should adjust the costs first and then calculate the energy consumption
+        lighting_cost = float(self.data["lighting-cost-current"])
+        heating_cost = float(self.data["heating-cost-current"])
+        hot_water_cost = float(self.data["hot-water-cost-current"])
+        total_cost = lighting_cost + heating_cost + hot_water_cost
+
+        adjusted_heating_cost = AnnualBillSavings.adjust_energy_cost_to_metered(
+            epc_energy_cost=heating_cost,
+            current_epc_rating=self.data["current-energy-rating"],
+        )
+
+        adjusted_hot_water_cost = AnnualBillSavings.adjust_energy_cost_to_metered(
+            epc_energy_cost=hot_water_cost,
+            current_epc_rating=self.data["current-energy-rating"],
+        )
+
+        adjusted_lighting_cost = AnnualBillSavings.adjust_energy_cost_to_metered(
+            epc_energy_cost=lighting_cost,
+            current_epc_rating=self.data["current-energy-rating"],
+        )
+
+        scoring_df["heating-cost-current"] = [adjusted_heating_cost]
+        scoring_df["hot-water-cost-current"] = [adjusted_hot_water_cost]
+        scoring_df["lighting-cost-current"] = [adjusted_lighting_cost]
+
         energy_consumption_client.data = None
         heating_prediction = energy_consumption_client.score_new_data(
             new_data=scoring_df, target="heating_kwh"
-        )
+        )[0]
 
         hot_water_prediction = energy_consumption_client.score_new_data(
             new_data=scoring_df, target="hot_water_kwh"
-        )
+        )[0]
 
-        starting_heat_demand = (
+        # We convert the lighting cost into kwh, just using the price cap
-            float(self.data["energy-consumption-current"]) * self.floor_area
+        lighting_kwh = float(adjusted_lighting_cost) / AnnualBillSavings.ELECTRICITY_PRICE_CAP
-        )
+
+        appliances_energy_use = AnnualBillSavings.estimate_appliances_energy_use(total_floor_area=self.floor_area)
+        appliances_energy_cost = appliances_energy_use * AnnualBillSavings.ELECTRICITY_PRICE_CAP
+
+        total_energy_consumption = heating_prediction + hot_water_prediction + lighting_kwh + appliances_energy_use
 
         self.current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered(
-            epc_energy_consumption=starting_heat_demand,
+            epc_energy_consumption=total_energy_consumption,
             current_epc_rating=self.data["current-energy-rating"],
-            total_floor_area=self.floor_area
         )
 
-        self.current_energy_bill = AnnualBillSavings.calculate_annual_bill(self.current_adjusted_energy)
+        self.energy_cost_estimates = {
+            "heating": adjusted_heating_cost,
+            "hot_water": adjusted_hot_water_cost,
+            "lighting": adjusted_lighting_cost,
+            "appliances": appliances_energy_cost
+        }
+
+        self.expected_energy_bill = (
+            adjusted_heating_cost + adjusted_hot_water_cost + adjusted_lighting_cost + appliances_energy_cost
+        )
 
     def set_spatial(self, spatial: pd.DataFrame):
         """

backend/ml_models/AnnualBillSavings.py

@@ -25,9 +25,9 @@ class AnnualBillSavings:
     AVERAGE_GAS_CONSUMPTION = 11500
 
     # Latest price cap figures from Ofgem are for April 2024
-    # https://www.ofgem.gov.uk/publications/new-energy-price-cap-level-april-june-2024-starts-today
+    # https://www.ofgem.gov.uk/energy-price-cap
-    ELECTRICITY_PRICE_CAP = 0.245
+    ELECTRICITY_PRICE_CAP = 0.2236
-    GAS_PRICE_CAP = 0.0604
+    GAS_PRICE_CAP = 0.0548
     # This is the most recent export payment figure, at 12p per kwh
     ELECTRICITY_EXPORT_PAYMENT = 0.12
 
@@ -125,7 +125,17 @@ class AnnualBillSavings:
         return eam
 
     @classmethod
-    def adjust_energy_to_metered(cls, epc_energy_consumption, current_epc_rating, total_floor_area):
+    def estimate_appliances_energy_use(cls, total_floor_area):
+        # The EPC energy consumption does not factor in cooking and applicance use, so this is estimated using the
+        # methodology outlined in SAP, and is discussed in the UCL paper in section 3.1.1
+        estimated_occupants = cls.calculate_occupants(total_floor_area=total_floor_area)
+        appliances_energy_use = cls.estimate_electrical_appliances(estimated_occupants, total_floor_area)
+        return appliances_energy_use
+
+    @classmethod
+    def adjust_energy_to_metered(
+        cls, epc_energy_consumption, current_epc_rating
+    ):
         """
         The over-prediction of energy use by EPCs in Great Britain: A comparison
         of EPC-modelled and metered primary energy use intensity
@@ -136,13 +146,6 @@ class AnnualBillSavings:
         :return:
         """
 
-        # The EPC energy consumption does not factor in cooking and applicance use, so this is estimated using the
-        # methodology outlined in SAP, and is discussed in the UCL paper in section 3.1.1
-        estimated_occupants = cls.calculate_occupants(total_floor_area=total_floor_area)
-        appliances_energy_use = cls.estimate_electrical_appliances(estimated_occupants, total_floor_area)
-
-        epc_energy_consumption += appliances_energy_use
-
         gradients = {
             "A": -0.1,
             "B": -0.1,
@@ -175,6 +178,52 @@ class AnnualBillSavings:
 
         return adjusted_consumption
 
+    @classmethod
+    def adjust_energy_cost_to_metered(cls, epc_energy_cost, current_epc_rating):
+        """
+        The over-prediction of energy use by EPCs in Great Britain: A comparison
+        of EPC-modelled and metered primary energy use intensity
+
+        Which can be found here: https://www.sciencedirect.com/science/article/pii/S0378778823002542
+        We implement the results on page 10
+
+        This is used to just re-map the cost from the EPC to the metered cost
+        :return:
+        """
+
+        gradients = {
+            "A": -0.1,
+            "B": -0.1,
+            "C": -0.43,
+            "D": -0.52,
+            "E": -0.7,
+            "F": -0.76,
+            "G": -0.76
+        }
+
+        intercepts = {
+            "A": 28,
+            "B": 28,
+            "C": 97,
+            "D": 119,
+            "E": 160,
+            "F": 157,
+            "G": 157
+        }
+
+        gradient = gradients[current_epc_rating]
+        intercept = intercepts[current_epc_rating]
+
+        # This should be negative
+        consumption_difference = gradient * epc_energy_cost + intercept
+        consumption_difference = 0 if consumption_difference > 0 else consumption_difference
+
+        adjusted_consumption = (epc_energy_cost + consumption_difference)
+        if adjusted_consumption < 0:
+            raise ValueError("consumption_difference should be negative")
+
+        return adjusted_consumption
+
     @classmethod
     def adjust_expected_band(cls, expected_epc_rating, current_epc_rating):
         """