From ebed7027ac721353593f089e015a9467ae6fa43e Mon Sep 17 00:00:00 2001
From: Khalim Conn-Kowlessar <kconnkowlessar@gmail.com>
Date: Sat, 15 Feb 2025 22:33:01 +0000
Subject: [PATCH] adding minimums for the number of SAP points solar PV will
 deliver

---
 backend/Property.py                       |  4 +++-
 recommendations/Recommendations.py        |  7 +++++++
 recommendations/SolarPvRecommendations.py | 21 ++++++++++++++++++++-
 3 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/backend/Property.py b/backend/Property.py
index e19970eb..eaffd54d 100644
--- a/backend/Property.py
+++ b/backend/Property.py
@@ -1259,7 +1259,9 @@ 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 the property is in a conservation area, is listed or is a heriage building, solar panels
+        # become a difficult measure to generally get through planning restrictions and so we do not recommend
+        # solar panels
         if self.restricted_measures:
             return False
 
diff --git a/recommendations/Recommendations.py b/recommendations/Recommendations.py
index 42f4e783..715332a5 100644
--- a/recommendations/Recommendations.py
+++ b/recommendations/Recommendations.py
@@ -623,6 +623,13 @@ class Recommendations:
                     if li_sap_limit is not None:
                         property_phase_impact["sap"] = min(property_phase_impact["sap"], li_sap_limit)
 
+                if rec["type"] == "solar_pv":
+                    # We use the SAP points in the recommendation as a minimum
+                    property_phase_impact["sap"] = (
+                        rec["sap_points"] if property_phase_impact["sap"] < rec["sap_points"] else
+                        property_phase_impact["sap"]
+                    )
+
                 # Insert this information into the recommendation.
                 if not rec.get("survey", False):
                     rec["sap_points"] = property_phase_impact["sap"]
diff --git a/recommendations/SolarPvRecommendations.py b/recommendations/SolarPvRecommendations.py
index 95f189d3..a97dbcb3 100644
--- a/recommendations/SolarPvRecommendations.py
+++ b/recommendations/SolarPvRecommendations.py
@@ -14,11 +14,16 @@ class SolarPvRecommendations:
     # This was previously set to 250w, but has been upped to 400 based on the systems used by Cotswolrd Energy Group
     SOLAR_PANEL_WATTAGE = 400
 
+    # For domestic properties, we don't recommend a solar PV system with wattage outside of these
+    # bounds
     MAX_SYSTEM_WATTAGE = 6000
     MIN_SYSTEM_WATTAGE = 1000
 
+    # the maximum area of root we allow to be covered in solar panels for our recommendations.
     MAX_ROOF_AREA_PERCENTAGE = 0.7
 
+    SAP_POINTS_PER_5_PERCENT_ROOF_COVERAGE = 1
+
     def __init__(self, property_instance):
         """
         :param property_instance: Instance of the Property class, for the home associated to property_id
@@ -212,6 +217,20 @@ class SolarPvRecommendations:
             roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / roof_area * 100)
             # We round up to the nearest 5
             roof_coverage_percent = np.ceil(roof_coverage_percent / 5) * 5
+
+            # Typically, we've observed that every 5% of additional roof coverage will result in at least
+            # an additional 1 SAP points (though often 2 points) Given this, we can add a reasonable minimum
+            # for the number of SAP points we might expect. We've observed that for some cases where properties
+            # are hitting the higher SAP scores (e.g. EPC A and above), the model can sometimes under-predict
+            # the number of SAP points. This appears to be due to a relatively small number of properties
+            # actually achieving the upper echelons of EPC rating. This can be the case if we're simulating a
+            # whole house retrofit where the home is getting complete insulation, a heat pump and solar panels.
+            # Because panels are the final recommendation, they are often the measure that takes the home
+            # into the medium to high EPC A ranges and so because of a lack of training data, this means that
+            # we might sometime under-predict. This minimum is intended to try and reduce the negative impact
+            # of this. This minimum is used in Recommendations.calculate_recommendation_impact
+            minimum_sap_points = (roof_coverage_percent / 5) * self.SAP_POINTS_PER_5_PERCENT_ROOF_COVERAGE
+
             for has_battery in [False, True]:
                 cost_result = self.costs.solar_pv(
                     has_battery=has_battery,
@@ -240,7 +259,7 @@ class SolarPvRecommendations:
                         "description": description,
                         "starting_u_value": None,
                         "new_u_value": None,
-                        "sap_points": None,
+                        "sap_points": minimum_sap_points,
                         "already_installed": already_installed,
                         **cost_result,
                         # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we