integrating solar api to router

.idea/Model.iml

@@ -7,7 +7,7 @@
       <sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
       <sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.10 (model_data)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyNamespacePackagesService">

.idea/misc.xml

@@ -3,7 +3,7 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.10 (backend)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (model_data)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
   <component name="PythonCompatibilityInspectionAdvertiser">
     <option name="version" value="3" />
   </component>

backend/apis/GoogleSolarApi.py

@@ -1,136 +1,19 @@
 import pandas as pd
 import numpy as np
 from recommendations.Costs import MCS_SOLAR_PV_COST_DATA
-
+from backend.ml_models.AnnualBillSavings import AnnualBillSavings
-from backend.Property import Property
-from backend.SearchEpc import SearchEpc
-from etl.epc.Record import EPCRecord
-from dotenv import load_dotenv
-from utils.s3 import read_dataframe_from_s3_parquet, read_from_s3
-import os
 import requests
-import msgpack
 from functools import lru_cache
 import time
 
-load_dotenv(dotenv_path="backend/.env")
-EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
-
-# This is for 6 Laura Close, Tintagel, PL34 0EB (same property that Cotswolrd energy used)
-uprn = 100040099104
-# This is for 353A, Hermitage Lane, ME16 9NT (one of the e.on properties)
-uprn = 200000964454
-# This is for 14 Victoria Road, Cross Hills, KEIGHLEY, North Yorkshire, ENGLAND, BD20 8SY
-uprn = 100050346517
-
-cleaning_data = read_dataframe_from_s3_parquet(
-    bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
-)
-
-searcher = SearchEpc(address1="", postcode="", uprn=uprn, auth_token=EPC_AUTH_TOKEN, os_api_key="")
-
-searcher.find_property(skip_os=True)
-
-epc_records = {
-    'original_epc': searcher.newest_epc.copy(),
-    'full_sap_epc': searcher.full_sap_epc.copy(),
-    'old_data': searcher.older_epcs.copy(),
-}
-
-epc = EPCRecord(
-    epc_records=epc_records,
-    run_mode="newdata",
-    cleaning_data=cleaning_data
-)
-
-uprn_filenames = read_dataframe_from_s3_parquet(
-    bucket_name="retrofit-data-dev", file_key="spatial/filename_meta.parquet"
-)
-
-p = Property(
-    id=0,
-    address=searcher.address_clean,
-    postcode=searcher.postcode_clean,
-    epc_record=epc,
-    already_installed={},
-    non_invasive_recommendations={},
-)
-
-p.get_spatial_data(uprn_filenames)
-
-cleaned = read_from_s3(
-    s3_file_name="cleaned_epc_data/cleaned.bson",
-    bucket_name="retrofit-data-dev"
-)
-
-cleaned = msgpack.unpackb(cleaned, raw=False)
-
-from etl.solar.SolarPhotoSupply import SolarPhotoSupply
-
-photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket="retrofit-data-dev")
-
-p.get_components(
-    cleaned=cleaned,
-    photo_supply_lookup=photo_supply_lookup,
-    floor_area_decile_thresholds=floor_area_decile_thresholds
-)
-p.hot_water_energy_source
-p.heating_energy_source
-
-longitude = p.spatial["longitude"]
-latitude = p.spatial["latitude"]
-
-api_key = "AIzaSyCIz8Psu5h-1txuDX0rQpUTgkvdj8yohqU"
-url = 'https://solar.googleapis.com/v1/solarPotential'
-params = {
-    'location.latitude': f'{latitude:.5f}',
-    'location.longitude': f'{longitude:.5f}',
-    'requiredQuality': "MEDIUM",
-    'key': api_key
-}
-
-insights_url = 'https://solar.googleapis.com/v1/buildingInsights:findClosest'
-
-# Make the GET request to the Solar API
-insights_response = requests.get(insights_url, params=params)
-insights_data = insights_response.json()
-
-solar_potential = insights_data["solarPotential"]
-
-from pprint import pprint
-
-pprint(solar_potential)
-
-# This is the maximum number of panels that can be installed
-solar_potential["maxArrayPanelsCount"]
-
-# This is the size of the panels used in the calculation - 400 watt
-solar_potential["panelCapacityWatts"]
-
-# Height of the panels used
-solar_potential["panelHeightMeters"]
-
-# Width of the panels used
-solar_potential["panelWidthMeters"]
-
-# This is the maximum area that can be covered by the panels
-solar_potential["maxArrayAreaMeters2"]
-
-# This is the area of the roof
-solar_potential["wholeRoofStats"]["areaMeters2"]
-
-# This is the area of the floor
-solar_potential["wholeRoofStats"]["groundAreaMeters2"]
-
-solar_potential["solarPanelConfigs"][0]
-solar_potential["solarPanelConfigs"][1]
-
-self = GoogleSolarApi(api_key=api_key)
-
 
 class GoogleSolarApi:
     NORTH_FACING_AZIMUTH_RANGE = (-30, 30)
 
+    # Conservative estimate of the proportion of electricity that will be consumed, whereas the rest will
+    # be exported
+    SOLAR_CONSUMPTION_PROPORTION = 0.5
+
     def __init__(self, api_key, max_retries=5):
         """
         Initialize the GoogleSolarApi class with the provided API key and maximum retries.
@@ -150,6 +33,8 @@ class GoogleSolarApi:
         self.roof_area = None
         self.roof_segment_indexes = None
         self.panel_area = None
+        self.panel_wattage = None
+        self.panel_performance = None
 
     def get_building_insights(self, longitude, latitude, required_quality="MEDIUM", max_retries=None):
         """
@@ -198,7 +83,6 @@ class GoogleSolarApi:
         :return: The JSON response containing the building insights data.
         """
 
-        # TODO - can we make a request which includes the 30cm buffer from the edge of the roof?
         self.insights_data = self.get_building_insights(longitude, latitude, required_quality)
 
         # Extract key data from the insights response
@@ -209,6 +93,7 @@ class GoogleSolarApi:
             self.insights_data["solarPotential"]["panelHeightMeters"] *
             self.insights_data["solarPotential"]["panelWidthMeters"]
         )
+        self.panel_wattage = self.insights_data["solarPotential"]["panelCapacityWatts"]
 
         # Automatically exclude north-facing segments
         self.exclude_north_facing_segments()
@@ -246,7 +131,8 @@ class GoogleSolarApi:
                         "generatedEnergy": generated_energy,
                         "ratio": ratio,
                         "n_panels": segment["panelsCount"],
-                        "cost": cost
+                        "cost": cost,
+                        "panneled_roof_area": self.panel_area * int(segment["panelsCount"])
                     }
                 )
 
@@ -263,12 +149,43 @@ class GoogleSolarApi:
                     "n_panels": roi_summary["n_panels"].sum(),
                     "total_energy": total_energy,
                     "total_cost": total_cost,
-                    "weighted_ratio": weighted_ratio
+                    "weighted_ratio": weighted_ratio,
+                    "panneled_roof_area": roi_summary["panneled_roof_area"].sum(),
+                    "array_warrage": roi_summary["n_panels"].sum() * self.panel_wattage
                 }
             )
 
         panel_performance = pd.DataFrame(panel_performance)
+        # We can have duplicate configurations
+        panel_performance = panel_performance.drop_duplicates()
+        # Ensure more than 4 panels
+        panel_performance = panel_performance[panel_performance["n_panels"] >= 4]
+        # Remove anything where the total energy is less than half of the array wattage
+        panel_performance = panel_performance[
+            (panel_performance["total_energy"] / panel_performance["array_warrage"]) >= 0.5
+            ]
+
+        # This first bracket is the value of the energy bill savings
+        panel_performance["bill_savings"] = (
+            self.SOLAR_CONSUMPTION_PROPORTION *
+            panel_performance["total_energy"] *
+            AnnualBillSavings.ELECTRICITY_PRICE_CAP
+        )
+        # This is the amount of energy exported
+        panel_performance["export_value"] = (
+            (1 - self.SOLAR_CONSUMPTION_PROPORTION) *
+            panel_performance["total_energy"] *
+            AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
+        )
+        panel_performance["energy_value"] = panel_performance["bill_savings"] + panel_performance["export_value"]
+        panel_performance["payback_years"] = panel_performance["total_cost"] / panel_performance["energy_value"]
+
         panel_performance = panel_performance.sort_values("weighted_ratio", ascending=False)
+        # TODO: Finish this!!
+
+        panel_performance["roof_area_percentage"] = panel_performance["panneled_roof_area"] / self.roof_area
+
+        self.panel_performance = panel_performance
 
     def exclude_north_facing_segments(self):
         """

backend/app/config.py

@@ -14,6 +14,7 @@ class Settings(BaseSettings):
     PLAN_TRIGGER_BUCKET: str
     EPC_AUTH_TOKEN: str
     ORDNANCE_SURVEY_API_KEY: str
+    GOOGLE_SOLAR_API_KEY: str
     DB_HOST: str
     DB_PASSWORD: str
     DB_USERNAME: str

backend/app/plan/router.py

@@ -29,6 +29,7 @@ from backend.app.utils import epc_to_sap_lower_bound, sap_to_epc
 
 from backend.ml_models.api import ModelApi
 from backend.Property import Property
+from backend.apis.GoogleSolarApi import GoogleSolarApi
 from etl.solar.SolarPhotoSupply import SolarPhotoSupply
 
 from recommendations.optimiser.CostOptimiser import CostOptimiser
@@ -347,10 +348,13 @@ async def trigger_plan(body: PlanTriggerRequest):
             bucket_name=get_settings().DATA_BUCKET, file_key="spatial/filename_meta.parquet"
         )
         photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket=get_settings().DATA_BUCKET)
+        solar_api_client = GoogleSolarApi(api_key=get_settings().GOOGLE_SOLAR_API_KEY)
 
         logger.info("Getting spatial data")
         for p in input_properties:
             p.get_spatial_data(uprn_filenames)
+            # Call Google Solar API
+            solar_api_client.get(longitude=p.spatial["longitude"], latitude=p.spatial["latitude"])
 
         logger.info("Getting components and epc recommendations")
         recommendations = {}

backend/ml_models/AnnualBillSavings.py

@@ -14,6 +14,8 @@ class AnnualBillSavings:
     # https://www.ofgem.gov.uk/publications/new-energy-price-cap-level-april-june-2024-starts-today
     ELECTRICITY_PRICE_CAP = 0.245
     GAS_PRICE_CAP = 0.0604
+    # This is the most recent export payment figure, at 12p per kwh
+    ELECTRICITY_EXPORT_PAYMENT = 0.12
 
     # This is a weighted mean of the price caps, using the consumption figures above as weights
     PRICE_FACTOR = 0.09549999999999999