switch off solar PV if property is listed/heritage or in a conservation area

backend/Property.py

@@ -395,6 +395,7 @@ class Property:
                     primary_recommendation_id=rec["recommendation_id"],
                     non_invasive_recommendations=self.non_invasive_recommendations,
                 )
+
                 self.recommendations_scoring_data.append(scoring_dict)
 
                 simulation_epc = self.epc_record.prepared_epc.copy()
@@ -1258,6 +1259,10 @@ class Property:
         if (self.building_id is not None) and (self.solar_panel_configuration is not None):
             return True
 
+        # If the property is in a conservation area, don't recommend
+        if self.restricted_measures:
+            return False
+
         is_valid_property_type = self.data["property-type"] in ["House", "Bungalow", "Maisonette"]
         is_valid_roof_type = (
             self.roof["is_flat"] or self.roof["is_pitched"] or self.roof["is_roof_room"]

backend/app/plan/router.py

@@ -639,8 +639,10 @@ async def trigger_plan(body: PlanTriggerRequest):
         recommendations_scoring_data = pd.DataFrame(recommendations_scoring_data)
 
         recommendations_scoring_data = recommendations_scoring_data.drop(
-            columns=["rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
-                     "carbon_ending"]
+            columns=[
+                "rdsap_change", "heat_demand_change", "carbon_change", "sap_ending", "heat_demand_ending",
+                "carbon_ending"
+            ]
         )
 
         all_predictions = await model_api.async_paginated_predictions(

etl/customers/lambeth/re-knocks.py

@@ -0,0 +1,23 @@
+import pandas as pd
+
+data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Downloads/Lambeth Reknocks.xlsx", sheet_name="Possible Route",
+    header=1
+)
+
+data["Outcomes"].value_counts()
+
+# Strip out: No
+
+df = data[data["Outcomes"] == "See notes"]
+notes_df = df[
+    ("Notes (If 'no answer' under outcomes, have you checked around the property for access issues where "
+     "possible?)")].value_counts().to_frame()
+
+example = df[df["Notes (If 'no answer' under outcomes, have you checked around the property for access issues where "
+                "possible?)"] == ('Access to rear of property only through number 10. Overgrown athe rear of property '
+                                  'installer wont be able to access')
+             ]
+
+# 18 did not attend
+#

etl/customers/stonewater/Wave 3 Preparation.py

@@ -4093,7 +4093,9 @@ def revised_model():
             'Main Wall Insulation', 'Secondary Wall Insulation', 'Loft insulation',
             'Flat Roof', 'Room in Roof', 'Window Upgrade', 'Door Upgrade',
             'Ventilation', 'Main Heating', 'Water Heating', 'Heating Controls',
-            'Solar PV', 'Other measures', 'Survey: Current SAP Rating', 'Survey: Current EPC Band',
+            'Solar PV', 'Other measures',
+            'Survey: Current SAP Rating',
+            'Survey: Current EPC Band',
             'Survey: Primary Energy Use (kWh/yr)',
             'Survey: Primary Energy Use Intensity (kWh/m2/yr)',
             'Survey: Number of Storeys', 'Survey: Fuel Bill',
@@ -4148,7 +4150,8 @@ def revised_model():
             'Best Match Organisation Reference',
             'Survey: Current EPC Band',
             'Survey: Current SAP Rating',
-            "Was Surveyed"
+            "Was Surveyed",
+            "match_confidence",
         ]
     ].rename(
         columns={
@@ -4157,11 +4160,13 @@ def revised_model():
             'Survey: Current SAP Rating': "Best Match - Survey: Current SAp Rating"
         }
     ).merge(
-        features[["Organisation Reference", "Walls", "Roofs", "Heating", "Main Fuel", "Age", "Property Type"]],
+        features[["Organisation Reference", "Walls", "Roofs", "Heating", "Main Fuel", "Age", "Property Type",
+                  "Total Floor Area"]],
         how="left",
         on="Organisation Reference"
     ).merge(
-        features[["Organisation Reference", "Walls", "Roofs", "Heating", "Main Fuel", "Age", "Property Type"]].rename(
+        features[["Organisation Reference", "Walls", "Roofs", "Heating", "Main Fuel", "Age", "Property Type",
+                  "Total Floor Area"]].rename(
             columns={
                 "Organisation Reference": "Best Match - Organisation Reference",
                 "Walls": "Best Match - Walls",
@@ -4169,7 +4174,8 @@ def revised_model():
                 "Heating": "Best Match - Heating",
                 "Main Fuel": "Best Match - Main Fuel",
                 "Age": "Best Match - Age",
-                "Property Type": "Best Match - Property Type"
+                "Property Type": "Best Match - Property Type",
+                "Total Floor Area": "Best Match - Total Floor Area"
             }
         ),
         how="left",
@@ -4180,7 +4186,8 @@ def revised_model():
                 "Organisation Reference", 'Survey: Main Wall Type', 'Survey: Main Wall Insulation',
                 'Survey: Main Roof Type', 'Survey: Main Roof Insulation', 'Survey: Main Roof Insulation Thickness',
                 'Survey: Existing Primary Heating System', 'Survey: Main Building Age Band',
-                'Survey: Main Building Wall Area (m2)',
+                'Survey: Main Building Wall Area (m2)', 'Survey: Total Floor Area (m2)',
+                'Survey: Main Building Age Band',
             ]
         ].rename(
             columns={
@@ -4203,7 +4210,12 @@ def revised_model():
             'Organisation Reference', 'Address', 'Postcode', 'Address ID', 'uprn', 'Archetype ID',
             'SAP', 'SAP Band', "Property Type", "Walls", "Roofs", 'Glazing',
             'Heating', 'Main Fuel', 'Hot Water', 'Number of matches', 'Proportion',
-            'Estimated SAP Rating', 'Estimated EPC Rating', "Was Surveyed"
+            'Estimated SAP Rating', 'Estimated EPC Rating', "Was Surveyed",
+            'Main Wall Insulation',
+            'Secondary Wall Insulation', 'Loft insulation', 'Flat Roof',
+            'Room in Roof', 'Window Upgrade', 'Door Upgrade', 'Ventilation',
+            'Main Heating', 'Water Heating', 'Heating Controls', 'Solar PV',
+            'Other measures', "2 of 3 Data Sources Have Consensus on EPC"
         ]
     ].rename(
         columns={
@@ -4271,7 +4283,7 @@ def revised_model():
     worksheet = worksheet.drop(columns=["Last EPC - uprn"])
 
     # Save to Excel with multiple sheets
-    excel_path = os.path.join(CUSTOMER_FOLDER_PATH, "Jan 2025 Project", "04022025 Stonewater Priority List.xlsx")
+    excel_path = os.path.join(CUSTOMER_FOLDER_PATH, "Jan 2025 Project", "13022025 Stonewater Priority List.xlsx")
     with pd.ExcelWriter(excel_path, engine="xlsxwriter") as writer:
         worksheet.to_excel(writer, sheet_name="Worksheet", index=False, header=True)
         mapped_lookup.to_excel(writer, sheet_name="Lookup Table", index=False, header=True)

etl/route_march_data_pull/app.py

@@ -258,18 +258,16 @@ def app():
     # - We want: fully insulated property (all wall types), EPC D or below (floors should be solid)
     # - Or the insulation required is loft/cavity (floors should be solid)
 
-    DATA_FOLDER = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Eastlight"
-    DATA_FILENAME = "Eastlight addresses potential PV data pull required.xlsx"
-    SHEET_NAME = "Sheet1"
-    POSTCODE_COLUMN = "Postcode"
-    FULLADDRESS_COLUMN = None
-    ADDRESS1_COLUMN = "HouseName"
+    DATA_FOLDER = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Stonewater"
+    DATA_FILENAME = "Stonewater  All Props for EPC Check 10.02.25.xlsx"
+    SHEET_NAME = "stonewater sap, insta"
+    POSTCODE_COLUMN = "Post Code"
+    FULLADDRESS_COLUMN = "Name"
+    ADDRESS1_COLUMN = "Name"
     ADDRESS1_METHOD = None
-    ADDRESS_COLS_TO_CONCAT = [
-        "HouseName", "Block", "Address1"
-    ]
+    ADDRESS_COLS_TO_CONCAT = []
     MISSING_POSTCODES_METHOD = None
-    PROPERTY_YEAR_BUILT = 'Built In Year'
+    PROPERTY_YEAR_BUILT = None
 
     # Maps addresses to uprn in problematic cases
     MANUAL_UPRN_MAP = {}
@@ -633,7 +631,7 @@ def app():
 
         # We want to deduce if flats have 50% of the properties below C75
         # We group by postcode and property type
-        grouped = asset_list.groupby(["Postcode", "Property Type"])
+        grouped = asset_list.groupby([POSTCODE_COLUMN, "Property Type"])
 
         flat_data = []
         for _, group in grouped:
@@ -643,7 +641,7 @@ def app():
 
                 flat_data.append(
                     {
-                        "Postcode": group["Postcode"].iloc[0],
+                        "Postcode": group[POSTCODE_COLUMN].iloc[0],
                         "Property Type": "Flat",
                         "Number of Flats with EPC": num_flats,
                         "Number of Flats below C75": num_below_c75,