Implemeted solar api wip

backend/Property.py

@@ -351,6 +351,7 @@ class Property:
                 if r["phase"] <= phase
             ]
             epc_transformations = [x["description_simulation"] for x in represenative_recs_to_this_phase]
+            
             # It is possible that we could have two simulations applied to the same descriptions
             # We extract these out
             phase_epc_transformation = {}

backend/app/plan/router.py

@@ -448,12 +448,13 @@ async def trigger_plan(body: PlanTriggerRequest):
                         p.set_solar_panel_configuration(unit_solar_panel_configuration)
 
         else:
-            # Model the solar potential at the property level
+            # # Model the solar potential at the property level
-            for p in input_properties:
+            # for p in input_properties:
-                # TODO: Complete me! - we probably won't do this for individual flats
+            #     # TODO: Complete me! - we probably won't do this for individual flats
-                solar_performance = solar_api_client.get(
+            #     solar_performance = solar_api_client.get(
-                    longitude=p.spatial["longitude"], latitude=p.spatial["latitude"]
+            #         longitude=p.spatial["longitude"], latitude=p.spatial["latitude"]
-                )
+            #     )
+            print("Implement me")
 
         logger.info("Getting components and epc recommendations")
         recommendations = {}

backend/app/plan/schemas.py

@@ -16,6 +16,7 @@ class PlanTriggerRequest(BaseModel):
 
     # Pre-defined list of possibilities for exclusions
     _allowed_exclusions = {
+        # Measure classes
         "wall_insulation",
         "ventilation",
         "roof_insulation",
@@ -25,7 +26,9 @@ class PlanTriggerRequest(BaseModel):
         "heating",
         "hot_water",
         "lighting",
-        "solar_pv"
+        "solar_pv",
+        # Specific measures
+        "air_source_heat_pump",
     }
 
     _allowed_goals = {"Increase EPC"}

etl/customers/vander_elliot/non_intrusives.py

@@ -119,11 +119,12 @@ def app():
         "portfolio_id": str(PORTFOLIO_ID),
         "housing_type": "Private",
         "goal": "Increase EPC",
-        "goal_value": "A",
+        "goal_value": "C",
         "trigger_file_path": filename,
         "already_installed_file_path": already_installed_filename,
         "patches_file_path": "",
         "non_invasive_recommendations_file_path": "",
+        "exclusions": ["wall_insulation", "air_source_heat_pump"],
         "budget": None,
     }
     print(body)

recommendations/HeatingControlRecommender.py

@@ -190,7 +190,11 @@ class HeatingControlRecommender:
                 "new_u_value": None,
                 "sap_points": None,
                 "already_installed": already_installed,
-                "simulation_config": simulation_config
+                "simulation_config": simulation_config,
+                "description_simulation": {
+                    "mainheatcont-description": "Programmer, room thermostat and TRVS",
+                    "mainheatc-energy-eff": "Good"
+                }
             }
         )
 
@@ -250,6 +254,10 @@ class HeatingControlRecommender:
                 "new_u_value": None,
                 "sap_points": None,
                 "already_installed": already_installed,
-                "simulation_config": simulation_config
+                "simulation_config": simulation_config,
+                "description_simulation": {
+                    "mainheatcont-description": "Time and temperature zone control",
+                    "mainheatc-energy-eff": "Very Good"
+                }
             }
         )

recommendations/HeatingRecommender.py

@@ -42,18 +42,21 @@ class HeatingRecommender:
 
         return self.has_electric_heating_description or electric_heating_assumed
 
-    def recommend(self, has_cavity_or_loft_recommendations, phase=0):
+    def recommend(self, has_cavity_or_loft_recommendations, phase=0, exclusions=None):
         """
         Produces heating recommendations
         :param has_cavity_or_loft_recommendations: boolean indicating if we have produced a cavity or loft insulation
         recommendation. If there are cavity or loft recommendations, the property would need to complete those measures
         before being able to get the boiler upgrade scheme benefits. The messaging in the front end would be to
         :param phase: indicates the phase of the retrofit programme
+        :param exclusions: A list of exclusions for the recommendations
         """
 
         # TODO: We could have a system flush recommendation for an existing boiler, where there is no need to replace
         #       the boiler, but instead flushing the system will make it run more efficiently. There is a cost for this
         #       in the Costs class, stored as SYSTEM_FLUSH_COST
+        
+        exclusions = [] if exclusions is None else exclusions
 
         self.heating_recommendations = []
         self.heating_control_recommendations = []
@@ -112,7 +115,7 @@ class HeatingRecommender:
         # In the future, we'll allow overrides, so that non-intrusive surveys can contradict these conditions
         # and either allow or prevent the recommendation of an air source heat pump
 
-        if self.is_ashp_valid():
+        if self.is_ashp_valid(exclusions=exclusions):
             self.recommend_air_source_heat_pump(
                 phase=phase, has_cavity_or_loft_recommendations=has_cavity_or_loft_recommendations
             )
@@ -182,11 +185,14 @@ class HeatingRecommender:
             description = ("Replace the existing boiler and cylinder without a thermostat with a new electric combi "
                            "boiler")
 
-    def is_ashp_valid(self):
+    def is_ashp_valid(self, exclusions):
 
         if "air_source_heat_pump" in self.property.non_invasive_recommendations:
             return True
 
+        if "air_source_heat_pump" in exclusions:
+            return False
+
         suitable_property_type = self.property.data["property-type"] in ["House", "Bungalow"]
         has_air_source_heat_pump = self.property.main_heating["has_air_source_heat_pump"]
 

recommendations/LightingRecommendations.py

@@ -109,8 +109,12 @@ class LightingRecommendations:
                 # For SAP points, we use the fact that lighting is usually worth 2 points and we scale this to
                 # the proportion of lights that will be set to low energy
                 "sap_points": round(2 * (number_non_lel_outlets / number_lighting_outlets), 2),
-                "heat_demand": heat_demand_change,
+                "kwh_savings": heat_demand_change,
                 "co2_equivalent_savings": carbon_change,
+                "description_simulation": {
+                    "lighting-energy-eff": "Very Good",
+                    "lighting-description": "Low energy lighting in all fixed outlets",
+                },
                 **cost_result
             }
         ]

recommendations/Recommendations.py

@@ -119,7 +119,9 @@ class Recommendations:
             has_cavity_or_loft_recommendations = len(cavity_or_loft_recommendations) > 0
 
             self.heating_recommender.recommend(
-                phase=phase, has_cavity_or_loft_recommendations=has_cavity_or_loft_recommendations
+                phase=phase,
+                has_cavity_or_loft_recommendations=has_cavity_or_loft_recommendations,
+                exclusions=self.exclusions
             )
             if (
                 self.heating_recommender.heating_recommendations or
@@ -294,6 +296,13 @@ class Recommendations:
         if rec["type"] != "solar_pv":
             return 0, 0
 
+        if property_instance.solar_panel_configuration is None:
+            print("PLACEHOLDER ESTIMATES")
+            # 50% reduction average
+            kwh_reduction = property_instance.energy_consumption_estimates["adjusted"]["appliances"] * 0.5
+            predicted_appliances_cost_reduction = kwh_reduction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
+            return predicted_appliances_cost_reduction, kwh_reduction
+
         # Calulate the amount of energy the solar panel array will generate for this unit
         unit_energy_consumption = (
             rec["initial_ac_kwh_per_year"] *
@@ -721,10 +730,14 @@ class Recommendations:
                     heating_kwh_reduction = 0 if predicted_heating_cost_reduction == 0 else (
                         phase_kwh_figures[previous_phase]["adjusted"]["heating"] - new_heating_kwh_adjusted
                     )
+                    if heating_kwh_reduction < 0:
+                        heating_kwh_reduction = 0
 
                     hot_water_kwh_reduction = 0 if predicted_hot_water_cost_reduction == 0 else (
                         phase_kwh_figures[previous_phase]["adjusted"]["hot_water"] - new_hot_water_kwh_adjusted
                     )
+                    if hot_water_kwh_reduction < 0:
+                        hot_water_kwh_reduction = 0
 
                     lighting_kwh_reduction = predicted_lighting_cost_reduction / AnnualBillSavings.ELECTRICITY_PRICE_CAP
 
@@ -773,7 +786,7 @@ class Recommendations:
                     # For the moment, we cap the number of SAP points that can be achieved by ventilation at 2
                     rec["sap_points"] = min(predicted_sap_points, LightingRecommendations.SAP_LIMIT)
                     rec["co2_equivalent_savings"] = min(predicted_co2_savings, rec["co2_equivalent_savings"])
-                    rec["heat_demand"] = min(predicted_heat_demand, rec["heat_demand"])
+                    rec["heat_demand"] = predicted_heat_demand
                 else:
                     rec["sap_points"] = predicted_sap_points
                     rec["co2_equivalent_savings"] = predicted_co2_savings

recommendations/SolarPvRecommendations.py

@@ -244,6 +244,7 @@ class SolarPvRecommendations:
                     # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
                     # back up here
                     "photo_supply": 100 * roof_coverage,
-                    "has_battery": has_battery
+                    "has_battery": has_battery,
+                    "description_simulation": {"photo-supply": 100 * roof_coverage},
                 }
             )

recommendations/WindowsRecommendations.py

@@ -115,5 +115,12 @@ class WindowsRecommendations:
                 "already_installed": already_installed,
                 **cost_result,
                 "is_secondary_glazing": is_secondary_glazing,
+                # TODO: Make this condition on is_secondary_glazing
+                "description_simulation": {
+                    "multi-glaze-proportion": 100,
+                    "windows-energy-eff": "Average",
+                    "windows-description": "Fully double glazed",
+                    "glazed-type": "double glazing installed during or after 2002",
+                }
             }
         ]