collected data for solar pv estimates

backend/Property.py

@@ -10,7 +10,6 @@ from etl.epc.settings import POTENTIAL_COLUMNS, EFFICIENCY_FEATURES, BUILT_FORM_
 from etl.epc_clean.epc_attributes.all_cleaners import all_cleaner_map
 from utils.logger import setup_logger
 from utils.s3 import read_dataframe_from_s3_parquet
-from epc_api.client import EpcClient
 from BaseUtility import Definitions
 from recommendations.rdsap_tables import england_wales_age_band_lookup, FLOOR_LEVEL_MAP
 from recommendations.recommendation_utils import (
@@ -89,6 +88,7 @@ class Property(Definitions):
         self.number_lighting_outlets = None
         self.floor_level = None
         self.number_of_windows = None
+        self.solar_pv_roof_area = None
 
         self.current_adjusted_energy = None
         self.expected_adjusted_energy = None
@@ -830,19 +830,16 @@ class Property(Definitions):
             extension_count=float(self.data["extension-count"]),
         )
 
-    def set_solar_panel_area(self, photo_supply_data):
+    def set_solar_panel_area(self, photo_supply_lookup, floor_area_decile_thresholds):
         """
         Sets the approximate area of the solar panels
         :return:
         """
 
-        # Approximate area of the solar panels
-        solar_panel_area = 1.6
-        # Wattage per pan
-        solar_panel_wattage = 360
-
-        photo_supply_lookup = photo_supply_data["photo_supply_lookup"]
-        floor_area_decile_thresholds = photo_supply_data["floor_area_decile_thresholds"]
+        if (self.insulation_floor_area is None) and (self.pitched_roof_area is None):
+            raise ValueError(
+                "Need to set insulation floor area and pitched roof area before setting solar pv roof area"
+            )
 
         # TODO: Create a class for the solar etl process and make this one of the functions, which applies a different
         #     method depending on the data type
@@ -868,4 +865,15 @@ class Property(Definitions):
             (photo_supply_lookup["is_roof_room"] == self.roof["is_roof_room"])
             ]
 
-        # n_panels = np.floor(solar_panel_area * )
+        if floor_area_decile in photo_supply_matched["floor_area_decile"].values:
+            photo_supply_matched = photo_supply_matched[
+                photo_supply_matched["floor_area_decile"] == floor_area_decile
+                ]
+
+        percentage_of_roof = photo_supply_matched["photo_supply_median"].mean()
+        percentage_of_roof = percentage_of_roof / 100
+
+        self.solar_pv_roof_area = (
+            self.insulation_floor_area * percentage_of_roof if self.roof["is_flat"] else
+            self.pitched_roof_area * percentage_of_roof
+        )

etl/testing_data/solar_research.py

@@ -81,8 +81,8 @@ def app():
 
     aggregated = results.groupby(
         [
-            "PROPERTY_TYPE", "BUILT_FORM", "TENURE", "is_pitched", "is_roof_room", "is_loft", "is_flat", "is_thatched",
-            "is_at_rafters", "has_dwelling_above", "CONSTRUCTION_AGE_BAND", "floor_area_decile"
+            "PROPERTY_TYPE", "BUILT_FORM", "TENURE", "is_pitched", "is_roof_room", "is_flat",
+            "CONSTRUCTION_AGE_BAND", "floor_area_decile"
         ],
         observed=True
     ).agg(
@@ -103,3 +103,10 @@ def app():
         bucket_name="retrofit-data-dev",
         file_key=f"solar_pv_supply/photo_supply_lookup.parquet",
     )
+
+    floor_area_decile_thresholds = pd.DataFrame(decile_thresholds, columns=["floor_area_decile_thresholds"])
+    save_dataframe_to_s3_parquet(
+        df=floor_area_decile_thresholds,
+        bucket_name="retrofit-data-dev",
+        file_key=f"solar_pv_supply/floor_area_decile_thresholds.parquet",
+    )

recommendations/Costs.py

@@ -18,6 +18,25 @@ regional_labour_variations = [
     {"Region": "Northern Ireland", "Adjustment_Factor": 0.76}
 ]
 
+# This data is based on the MCS database
+MCS_SOLAR_PV_COST_DATA = {
+    "last_updated": "2024-01-04",
+    "average_cost_per_kwh": 2013.94,
+    "average_cost_per_kwh-Outer London": 2618.75,
+    "average_cost_per_kwh-Inner London": 2618.75,
+    "average_cost_per_kwh-South East England": 2083.33,
+    "average_cost_per_kwh-South West England": 2113,
+    "average_cost_per_kwh-East of England": 1973.86,
+    "average_cost_per_kwh-East Midlands": 1981.86,
+    "average_cost_per_kwh-West Midlands": 1926.55,
+    "average_cost_per_kwh-North East England": 2028.49,
+    "average_cost_per_kwh-North West England": 1620.42,
+    "average_cost_per_kwh-Yorkshire and the Humber": 2060.9,
+    "average_cost_per_kwh-Wales": 1898.83,
+    "average_cost_per_kwh-Scotland": 1967.97,
+    "average_cost_per_kwh-Northern Ireland": 2126.09,
+}
+
 
 class Costs:
     """
@@ -811,3 +830,6 @@ class Costs:
             "labour_cost": labour_cost,
             "labour_days": labour_days
         }
+
+    def solar_pv(self, wattage):
+        pass

recommendations/SolarPvRecommendations.py

@@ -1,12 +1,16 @@
+import numpy as np
 from recommendations.Costs import Costs
 
 
 class SolarPvRecommendations:
+    # Approximate area of the solar panels
+    SOLAR_PANEL_AREA = 1.6
+    # Wattage per panel
+    SOLAR_PANEL_WATTAGE = 360
 
     def __init__(self, property_instance):
         """
         :param property_instance: Instance of the Property class, for the home associated to property_id
-        :param photo_supply_lookup: Lookup table of photo supply percentages
         """
 
         self.property = property_instance
@@ -35,3 +39,8 @@ class SolarPvRecommendations:
             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_capacity = number_solar_panels * self.SOLAR_PANEL_WATTAGE
+
+        # Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
+        # of solar PV installations