From 69981192089e6256df5f4918c6ec244037be4c05 Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Tue, 21 May 2024 12:10:38 +0100
Subject: [PATCH] added solar to mds

---
 backend/apis/GoogleSolarApi.py            |  6 ++--
 recommendations/Mds.py                    |  5 +--
 recommendations/SolarPvRecommendations.py | 40 +++++++++++++++++++++++
 3 files changed, 47 insertions(+), 4 deletions(-)

diff --git a/backend/apis/GoogleSolarApi.py b/backend/apis/GoogleSolarApi.py
index 86324c58..205a3560 100644
--- a/backend/apis/GoogleSolarApi.py
+++ b/backend/apis/GoogleSolarApi.py
@@ -11,12 +11,14 @@ 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
 
 cleaning_data = read_dataframe_from_s3_parquet(
     bucket_name="retrofit-data-dev", file_key="sap_change_model/cleaning_dataset.parquet",
 )
 
-searcher = SearchEpc(address1="6 Laura Close", postcode="PL34 0EB", uprn=uprn, auth_token=EPC_AUTH_TOKEN, os_api_key="")
+searcher = SearchEpc(address1="", postcode="", uprn=uprn, auth_token=EPC_AUTH_TOKEN, os_api_key="")
 
 searcher.find_property(skip_os=True)
 
@@ -80,7 +82,7 @@ solar_potential["panelWidthMeters"]
 
 solar_potential["wholeRoofStats"]
 
-# Copy of response for testing:
+# Copy of response for testing - 6 Laura Close, Tintagel, PL34 0EB
 # {'name': 'buildings/ChIJ2yC6t4KEa0gRh2TIssogI7k', 'center': {'latitude': 50.667375, 'longitude': -4.7416833},
 # 'imageryDate': {'year': 2021, 'month': 7, 'day': 19}, 'regionCode': 'GB', 'solarPotential': {'maxArrayPanelsCount':
 # 39, 'maxArrayAreaMeters2': 76.578636, 'maxSunshineHoursPerYear': 1172.0627, 'carbonOffsetFactorKgPerMwh':
diff --git a/recommendations/Mds.py b/recommendations/Mds.py
index ca6ee3db..ecc8c852 100644
--- a/recommendations/Mds.py
+++ b/recommendations/Mds.py
@@ -123,8 +123,9 @@ class Mds:
             self.heating_recommender.recommend(phase=0)
 
         if "solar_pv" in measure_config_list:
-            raise Exception("check me out 14")
-            self.solar_recommender.recommend(phase=0)
+            recs = self.solar_recommender.mds_recommend(phase=0, solar_pv_percentage=0.5)
+            recs = self.insert_recommendation_id(recs, measures, "solar_pv")
+            mds_recommendations.append(recs)
 
         if "double_glazing" in measure_config_list:
             raise Exception("check me out 15")
diff --git a/recommendations/SolarPvRecommendations.py b/recommendations/SolarPvRecommendations.py
index 58d4b123..14161da3 100644
--- a/recommendations/SolarPvRecommendations.py
+++ b/recommendations/SolarPvRecommendations.py
@@ -35,6 +35,46 @@ class SolarPvRecommendations:
 
         return trimmed_list
 
+    def mds_recommend(self, phase=None, solar_pv_percentage=0.5):
+        # For specific usage within the mds report
+
+        solar_pv_roof_area = self.property.get_solar_pv_roof_area(solar_pv_percentage)
+
+        number_solar_panels = np.floor(solar_pv_roof_area / self.SOLAR_PANEL_AREA)
+        solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE
+
+        solar_panel_wattage = np.clip(
+            a=solar_panel_wattage, a_min=self.MIN_SYSTEM_WATTAGE, a_max=self.MAX_SYSTEM_WATTAGE
+        )
+
+        # We now have a property which is potentially suitable for solar PV
+        roof_coverage_percent = round(solar_pv_percentage * 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=False)
+        kw = np.floor(solar_panel_wattage / 100) / 10
+
+        description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
+                       f"anel system on {round(roof_coverage_percent)}% the roof.")
+
+        return [
+            {
+                "phase": phase,
+                "parts": [],
+                "type": "solar_pv",
+                "description": description,
+                "starting_u_value": None,
+                "new_u_value": None,
+                "sap_points": None,
+                "already_installed": False,
+                **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": roof_coverage_percent,
+                "has_battery": False
+            }
+        ]
+
     def recommend(self, phase):
         """
         We check if a property is potentially suitable for solar PV based on the following criteria: