implemented further scenarios into solar recommendations

backend/Property.py

@@ -118,7 +118,6 @@ class Property:
         self.number_lighting_outlets = epc_record.prepared_epc.get("fixed_lighting_outlets_count")
         self.floor_level = None
         self.number_of_windows = None
-        self.solar_pv_roof_area = None
         self.solar_pv_percentage = None
 
         self.current_adjusted_energy = None
@@ -185,6 +184,8 @@ class Property:
             recommendation_record["walls_insulation_thickness_ending"] = "above average"
             recommendation_record["walls_energy_eff_ending"] = "Good"
 
+            # Note: often when the wall is insulatied, the internal/external insulation is not noted so we should
+            #       test the impact of using these booleans
             if recommendation["type"] == "external_wall_insulation":
                 recommendation_record["external_insulation"] = True
                 recommendation_record["internal_insulation"] = False
@@ -238,7 +239,10 @@ class Property:
 
             recommendation_record["roof_insulation_thickness_ending"] = str(proposed_depth)
             if recommendation["type"] == "loft_insulation":
-                recommendation_record["roof_energy_eff_ending"] = "Good"
+                if proposed_depth >= 270:
+                    recommendation_record["roof_energy_eff_ending"] = "Very Good"
+                else:
+                    recommendation_record["roof_energy_eff_ending"] = "Good"
             else:
                 recommendation_record["roof_energy_eff_ending"] = "Very Good"
         else:
@@ -682,9 +686,16 @@ class Property:
         percentage_of_roof = photo_supply_matched["photo_supply_median"].mean()
         percentage_of_roof = percentage_of_roof / 100
 
-        self.solar_pv_roof_area = (
+        self.solar_pv_percentage = percentage_of_roof
+
+    def get_solar_pv_roof_area(self, percentage_of_roof):
+        """
+        Given a percentage of the roof, this method will return the estimated area of the solar panels
+        :param percentage_of_roof:
+        :return:
+        """
+
+        return (
             self.insulation_floor_area * percentage_of_roof if self.roof["is_flat"] else
             self.pitched_roof_area * percentage_of_roof
         )
-
-        self.solar_pv_percentage = percentage_of_roof

backend/ml_models/Valuation.py

@@ -51,13 +51,14 @@ class PropertyValuation:
     KNIGHT_FRANK_MAPPING = [
         {"start": "D", "end": "C", "increase_percentage": 0.03},
         {"start": "D", "end": "B", "increase_percentage": 0.088},
+        {"start": "D", "end": "A", "increase_percentage": 0.088},
     ]
 
     NATIONWIDE_MAPPING = [
-        {"start": "G", "end": "D", "increase_percentage": 0.035},
-        {"start": "F", "end": "D", "increase_percentage": 0.035},
-        {"start": "D", "end": "B", "increase_percentage": 0.017},
-        {"start": "D", "end": "A", "increase_percentage": 0.017},
+        # {"start": "G", "end": "D", "increase_percentage": 0.035},
+        # {"start": "F", "end": "D", "increase_percentage": 0.035},
+        # {"start": "D", "end": "B", "increase_percentage": 0.017},
+        # {"start": "D", "end": "A", "increase_percentage": 0.017},
     ]
 
     EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]

recommendations/Costs.py

@@ -37,6 +37,9 @@ MCS_SOLAR_PV_COST_DATA = {
     "average_cost_per_kwh-Northern Ireland": 2126.09,
 }
 
+# This is based on quotes from installers
+BATTERY_COST = 3500
+
 
 class Costs:
     """
@@ -835,7 +838,7 @@ class Costs:
             "labour_days": labour_days
         }
 
-    def solar_pv(self, wattage: float):
+    def solar_pv(self, wattage: float, has_battery: bool = False):
 
         """
         Calculates the total cost for solar PV based data provided by the MCS dashboard, which contains
@@ -847,8 +850,8 @@ class Costs:
 
         Price can also be benchmarked against this checkatrade article:
         https://www.checkatrade.com/blog/cost-guides/cost-of-solar-panel-installation/
-        :param wattage: Peak wattage of the solar PV system
-        :return:
+        :param wattage: Peak wattage of the solar PV system]
+        :param has_battery: Bool, whether the system includes a battery
         """
 
         # Get the cost data relevant to the region
@@ -858,6 +861,11 @@ class Costs:
         total_cost = kw * regional_cost
 
         subtotal_before_vat = total_cost / (1 + self.VAT_RATE)
+
+        if has_battery:
+            # The battery cost is based on the £3500 quote, recieved from installers
+            subtotal_before_vat += BATTERY_COST
+            
         vat = total_cost - subtotal_before_vat
 
         # Labour hours are based on estimates from online research but an average team seems to consist of 3 people

recommendations/Recommendations.py

@@ -44,6 +44,9 @@ class Recommendations:
 
         """
         This method runs the recommendations for the individual measures and then appends them to a list for output
+
+        The recommendations are implemented in order of suggested phase, from fabric first to heating systems, to
+        renewables.
         :return:
         """
 

recommendations/SolarPvRecommendations.py

@@ -39,17 +39,56 @@ class SolarPvRecommendations:
         if not is_valid_property_type or not is_valid_roof_type or not has_no_existing_solar_pv:
             return
 
-        # We now have a property which is potentially suitable for solar PV
-        number_solar_panels = np.floor(self.property.solar_pv_roof_area / self.SOLAR_PANEL_AREA)
-        solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE
+        # For the solar recommendations, we produce the following scenarios:
+        # 1) Solar panels only, we present a high, medium and low coverage
+        # 2) With and without battery
+        roof_coverage_scenarios = [
+            self.property.solar_pv_percentage - 0.1, self.property.solar_pv_percentage,
+            self.property.solar_pv_percentage + 0.1
+        ]
+        # We make sure we haven't gone too low or high
+        roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 1]
+        battery_scenarios = [False, True]
 
-        roof_coverage_percent = round(self.property.solar_pv_percentage * 100)
+        # I now produce the cross product of the scenarios
+        scenarios = [(roof, battery) for roof in roof_coverage_scenarios for battery in battery_scenarios]
 
-        # Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
-        # of solar PV installations
-        cost_result = self.costs.solar_pv(wattage=solar_panel_wattage)
+        for roof_coverage, has_battery in scenarios:
+            # We now have a property which is potentially suitable for solar PV
+            solar_pv_roof_area = self.property.get_solar_pv_roof_area(roof_coverage)
 
-        kw = np.floor(solar_panel_wattage / 100) / 10
+            number_solar_panels = np.floor(solar_pv_roof_area / self.SOLAR_PANEL_AREA)
+            solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE
+
+            roof_coverage_percent = round(roof_coverage * 100)
+
+            # Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
+            # of solar PV installations
+            cost_result = self.costs.solar_pv(wattage=solar_panel_wattage, has_battery=has_battery)
+
+            kw = np.floor(solar_panel_wattage / 100) / 10
+
+            if has_battery:
+                description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on "
+                               f"{round(roof_coverage_percent * 100)}% the roof, with a battery storage system.")
+            else:
+                description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
+                               f"anel system on {round(roof_coverage_percent * 100)}% the roof.")
+
+            self.recommendation.append(
+                {
+                    "parts": [],
+                    "type": "solar_pv",
+                    "description": description,
+                    "starting_u_value": None,
+                    "new_u_value": None,
+                    "sap_points": None,
+                    **cost_result,
+                    # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
+                    # back up here
+                    "photo_supply": 100 * scenario
+                }
+            )
 
         self.recommendation = [
             {