got the recommendation impact working for pfp

backend/Property.py

@@ -739,7 +739,7 @@ class Property:
         self.current_adjusted_energy = (
             adjusted_heating_kwh + adjusted_hot_water_kwh + adjusted_lighting_kwh + adjusted_applicances_kwh
         )
-        self.expected_energy_bill = (
+        self.current_energy_bill = (
             adjusted_heating_cost + adjusted_hot_water_cost + adjusted_lighting_cost + adjusted_appliances_cost
         )
 

backend/app/plan/router.py

@@ -501,7 +501,9 @@ async def trigger_plan(body: PlanTriggerRequest):
                 Recommendations.calculate_recommendation_impact(
                     property_instance=property_instance,
                     all_predictions=all_predictions,
-                    recommendations=recommendations
+                    recommendations=recommendations,
+                    representative_recommendations=representative_recommendations,
+                    energy_consumption_client=energy_consumption_client
                 )
             )
 

recommendations/Recommendations.py

@@ -277,9 +277,71 @@ class Recommendations:
 
         return property_recommendations
 
+    @staticmethod
+    def _calculate_appliance_solar_savings(
+        rec, property_instance, heating_kwh_reduction, hot_water_kwh_reduction, lighting_kwh_reduction
+    ):
+        """
+        Calculates the impact on kwh and cost of installing solar panels on appliances
+        :param rec: The recommendation
+        :param property_instance: Instance of the Property class
+        :param heating_kwh_reduction: The kwh reduction from heating
+        :param hot_water_kwh_reduction: The kwh reduction from hot water
+        :param lighting_kwh_reduction: The kwh reduction from lighting
+        :return:
+        """
+
+        if rec["type"] != "solar_pv":
+            return 0, 0
+
+        # Calulate the amount of energy the solar panel array will generate for this unit
+        unit_energy_consumption = (
+            rec["initial_ac_kwh_per_year"] *
+            property_instance.solar_panel_configuration["unit_share_of_energy"]
+        )
+
+        unit_energy_utilised = unit_energy_consumption * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION
+        unit_energy_exported = unit_energy_consumption - unit_energy_utilised
+        unit_energy_exported_value = unit_energy_exported * AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
+
+        # We assume that 50% of the energy generated will be used by the property without a battery
+        # to be conservative
+
+        # of the energy utilised, some of it is used by heating, hot water and lighting so we
+        # remove that from the total
+        unit_energy_utilised -= (
+            heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
+        )
+        unit_energy_utilised = 0 if unit_energy_utilised < 0 else unit_energy_utilised
+
+        # This is how much energy the appliances will use after install
+        post_install_appliance_kwh = (
+            property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
+            unit_energy_utilised
+        )
+        post_install_appliance_kwh = (
+            0 if post_install_appliance_kwh < 0 else post_install_appliance_kwh
+        )
+
+        predicted_appliances_kwh_reduction = (
+            property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
+            post_install_appliance_kwh
+        )
+
+        predicted_appliances_cost_reduction = unit_energy_exported_value + (
+            predicted_appliances_kwh_reduction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
+        )
+
+        return predicted_appliances_cost_reduction, predicted_appliances_kwh_reduction
+
     @classmethod
     def calculate_recommendation_impact(
-        cls, property_instance, all_predictions, recommendations, energy_consumption_client
+        cls,
+        property_instance,
+        all_predictions,
+        recommendations,
+        representative_recommendations,
+        energy_consumption_client
     ):
 
         """
@@ -289,6 +351,7 @@ class Recommendations:
         :param property_instance: Instance of the Property class, for the home associated to property_id
         :param all_predictions: dictionary of predictions from the model apis
         :param recommendations: dictionary of recommendations for the property
+        :param representative_recommendations: dictionary of representative recommendations for the property
         :param energy_consumption_client: Instance of the EnergyConsumptionClient class
         :return:
         """
@@ -350,8 +413,14 @@ class Recommendations:
             ].median().reset_index()
         )
 
+        representative_rec_ids = [
+            rec["recommendation_id"] for rec in representative_recommendations[property_instance.id]
+        ]
+
         phase_lighting_costs = {}
         phase_kwh_figures = {}
+        bill_savings_list = []
+        kwh_savings_list = []
         for recommendations_by_type in property_recommendations:
             for rec in recommendations_by_type:
 
@@ -447,21 +516,6 @@ class Recommendations:
                         "unadjusted": scoring_lighting_cost
                     }
 
-                    # This is the total bill savings for the recommendation
-                    if rec["type"] == "solar_pv":
-                        # We need to calculate the predicted bill savings for the solar pv recommendation
-                        # where we will get some savings from the cost of appliances but it depends on the amount
-                        # of energy generated by the solar panels
-                        # We can assume that 50% of the energy generated will be used by the property without
-                        # a battery, to be conservative.
-                        # SIMILARLY: We need to handle kwh savings
-                        raise Exception("Handle me")
-                    else:
-                        predicted_bill_savings = (
-                            predicted_heating_cost_reduction + predicted_hot_water_cost_reduction +
-                            predicted_lighting_cost_reduction
-                        )
-
                     # We now predict the kwh savings using the xgb model
 
                     simulation_epc = property_instance.simulation_epcs[rec["phase"]].copy()
@@ -507,7 +561,30 @@ class Recommendations:
 
                     lighting_kwh_reduction = predicted_lighting_cost_reduction / AnnualBillSavings.ELECTRICITY_PRICE_CAP
 
-                    kwh_reduction = heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
+                    (
+                        predicted_appliances_cost_reduction,
+                        predicted_appliances_kwh_reduction
+                    ) = cls._calculate_appliance_solar_savings(
+                        rec=rec,
+                        property_instance=property_instance,
+                        heating_kwh_reduction=heating_kwh_reduction,
+                        hot_water_kwh_reduction=hot_water_kwh_reduction,
+                        lighting_kwh_reduction=lighting_kwh_reduction
+                    )
+
+                    kwh_reduction = (
+                        heating_kwh_reduction +
+                        hot_water_kwh_reduction +
+                        lighting_kwh_reduction +
+                        predicted_appliances_kwh_reduction
+                    )
+
+                    predicted_bill_savings = (
+                        predicted_heating_cost_reduction +
+                        predicted_hot_water_cost_reduction +
+                        predicted_lighting_cost_reduction +
+                        predicted_appliances_cost_reduction
+                    )
 
                     phase_kwh_figures[rec["phase"]] = {
                         "adjusted": {
@@ -651,48 +728,16 @@ class Recommendations:
 
                     lighting_kwh_reduction = predicted_lighting_cost_reduction / AnnualBillSavings.ELECTRICITY_PRICE_CAP
 
-                    # This is the total bill savings for the recommendation
+                    (
-
+                        predicted_appliances_cost_reduction,
-                    predicted_appliances_cost_reduction = 0
+                        predicted_appliances_kwh_reduction
-                    predicted_appliances_kwh_reduction = 0
+                    ) = cls._calculate_appliance_solar_savings(
-                    if rec["type"] == "solar_pv":
+                        rec=rec,
-                        # Calulate the amount of energy the solar panel array will generate for this unit
+                        property_instance=property_instance,
-                        unit_energy_consumption = (
+                        heating_kwh_reduction=heating_kwh_reduction,
-                            rec["initial_ac_kwh_per_year"] *
+                        hot_water_kwh_reduction=hot_water_kwh_reduction,
-                            property_instance.solar_panel_configuration["unit_share_of_energy"]
+                        lighting_kwh_reduction=lighting_kwh_reduction
-                        )
+                    )
-
-                        unit_energy_utilised = unit_energy_consumption * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION
-                        unit_energy_exported = unit_energy_consumption - unit_energy_utilised
-                        unit_energy_exported_value = unit_energy_exported * AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
-
-                        # We assume that 50% of the energy generated will be used by the property without a battery
-                        # to be conservative
-
-                        # of the energy utilised, some of it is used by heating, hot water and lighting so we
-                        # remove that from the total
-                        unit_energy_utilised -= (
-                            heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
-                        )
-                        unit_energy_utilised = 0 if unit_energy_utilised < 0 else unit_energy_utilised
-
-                        # This is how much energy the appliances will use after install
-                        post_install_appliance_kwh = (
-                            property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
-                            unit_energy_utilised
-                        )
-                        post_install_appliance_kwh = (
-                            0 if post_install_appliance_kwh < 0 else post_install_appliance_kwh
-                        )
-
-                        predicted_appliances_kwh_reduction = (
-                            property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
-                            post_install_appliance_kwh
-                        )
-
-                        predicted_appliances_cost_reduction = unit_energy_exported_value + (
-                            predicted_appliances_kwh_reduction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
-                        )
 
                     # We now calculate the predicted_bill_savings
                     predicted_bill_savings = (
@@ -723,17 +768,18 @@ class Recommendations:
                 rec["kwh_savings"] = kwh_reduction
                 rec["energy_cost_savings"] = predicted_bill_savings
 
+                if rec["recommendation_id"] in representative_rec_ids:
+                    bill_savings_list.append(predicted_bill_savings)
+                    kwh_savings_list.append(kwh_reduction)
+
                 if (rec["sap_points"] is None) and (rec["co2_equivalent_savings"] is None) or (
                     rec["heat_demand"] is None) or (rec["energy_cost_savings"] is None):
                     raise ValueError("sap points, co2 or heat demand is missing")
 
         # We sum up the total savings for the property and that is our expected energy bill
-        # expected_energy_bill = sum(
+
-        #     [
+        expected_energy_bill = property_instance.current_energy_bill - sum(bill_savings_list)
-        #         rec["energy_cost_savings"] for rec in property_recommendations
+        expected_adjusted_energy = property_instance.current_adjusted_energy - sum(kwh_savings_list)
-        #         if rec["type"] != "mechanical_ventilation"
-        #     ]
-        # )
 
         return (
             property_recommendations,