insert phase metrics into the simualation epc

backend/Property.py

@@ -22,6 +22,7 @@ from recommendations.recommendation_utils import (
     estimate_windows,
 )
 from backend.ml_models.AnnualBillSavings import AnnualBillSavings
+from backend.app.utils import sap_to_epc
 
 ENVIRONMENT = os.environ.get("ENVIRONMENT", "dev")
 DATA_BUCKET = os.environ.get(
@@ -442,6 +443,39 @@ class Property:
             simulation_epc.update(phase_epc_transformation)
             self.simulation_epcs[phase] = simulation_epc
 
+    def update_simulation_epcs(self, impact_summary):
+        """
+        This method will insert the high level measures, such as SAP, heat demand, carbon, etc
+        :return:
+        """
+        if self.simulation_epcs is None:
+            raise ValueError("Simulation EPCs have not been created")
+
+        phases = sorted(list(self.simulation_epcs.keys()))
+        updated_simulation_epcs = []
+        for phase in phases:
+            sim_epc = self.simulation_epcs[phase].copy()
+            phase_impact = [x for x in impact_summary if x["phase"] == phase][0]
+            # We update all of the features that should have an impact on the kwh model
+
+            sim_epc.update(
+                {
+                    "heating-cost-current": phase_impact["unadjusted_heating_cost"],
+                    "hot-water-cost-current": phase_impact["unadjusted_hot_water_cost"],
+                    # CO₂ emissions per square metre floor area per year in kg/m². Since CO₂ emissions are in tonnes
+                    # per year, we multiply by 1000 to get kg/m²
+                    "co2-emiss-curr-per-floor-area": round(
+                        1000 * (phase_impact["carbon"] / self.data["total-floor-area"])
+                    ),
+                    "co2-emissions-current": phase_impact["carbon"],
+                    "current-energy-rating": sap_to_epc(phase_impact["sap"]),
+                    "current-energy-efficiency": int(np.floor(phase_impact["sap"])),
+                    "current-energy-cost": phase_impact["unadjusted_energy_cost"],
+                    "energy-consumption-current": phase_impact["heat_demand"],
+                    "lighting-cost-current": phase_impact["unadjusted_lighting_cost"],
+                }
+            )
+
     @staticmethod
     def create_recommendation_scoring_data(
         property_id,

backend/app/plan/router.py

@@ -800,6 +800,7 @@ async def trigger_plan(body: PlanTriggerRequest):
         #       possibility with heating system
         # TODO: After optimising, if there are any cheap, quick win measures (e.g. insulate water tank with hot water
         #      cylinder jacket), we should add these to the recommendations as default
+        raise Exception("Add the cost impacts into the cost model")
         logger.info("Optimising recommendations")
         for property_id in recommendations.keys():
 
@@ -813,6 +814,10 @@ async def trigger_plan(body: PlanTriggerRequest):
                 )
             )
 
+            # We use the impact_summary to update the simulation_epcs with the new SAP, heat demand, carbon, cost etc
+            # at each phase
+            property_instance.update_simulation_epcs(impact_summary)
+
             input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
 
             current_sap_points = int(property_instance.data["current-energy-efficiency"])

recommendations/Recommendations.py

@@ -500,6 +500,9 @@ class Recommendations:
                 previous_phase_values.update(previous_phase_unadjusted_costs)
 
                 # We extract the values for the current phase
+                # TODO: For things like lighting costs for heating and hot water recommendations, we should actually
+                #       update phase_cost since the phase cost should be the same as the previous phase
+
                 current_phase_values = {
                     "sap": phase_energy_efficiency_metrics["sap_change"],
                     "carbon": phase_energy_efficiency_metrics["carbon_change"],