roughly implemented hot water insulation jacket tank insulation

etl/epc_clean/app.py

@@ -35,6 +35,7 @@ def app():
 
     cleaned_data = {}
     epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
+
     for directory in tqdm(epc_directories):
         data = pd.read_csv(directory / "certificates.csv", low_memory=False)
         # Rename the columns to the same format as the api returns

recommendations/Costs.py

@@ -980,3 +980,21 @@ class Costs:
             "labour_hours": 4,
             "labour_days": 1,
         }
+
+    def hot_water_tank_insulation(self):
+        """
+        Calculate the cost of installing hot water tank insulation
+        """
+
+        # The £50 cost is a rough estimate based on internet research
+        total_cost = 50
+        subtotal_before_vat = total_cost / (1 + self.VAT_RATE)
+        vat = total_cost - subtotal_before_vat
+
+        return {
+            "total": total_cost,
+            "subtotal": subtotal_before_vat,
+            "vat": vat,
+            "labour_hours": 0,
+            "labour_days": 0,
+        }

recommendations/HotwaterRecommendations.py

@@ -7,7 +7,47 @@ class HotwaterRecommendations:
         self.property = property_instance
         self.costs = Costs(self.property)
 
-        self.recommendation = []
+        self.recommendations = []
 
-    def recommend(self):
-        pass
+    def recommend(self, phase):
+        """
+        There are maybe a number of recommendations that are simultaneously applicable to the property.
+        If this is true then the phase may need to be incrememnted from within this recommendation
+
+        :param phase:
+        :return:
+        """
+        # Reset the recommendations
+        self.recommendations = []
+
+        # This first iteration of the recommender will provide very basic recommendation
+        # We recommend heating controls based on the main heating system
+        if (self.property.hotwater["heater_type"] in ["electric immersion"]) & \
+            (self.property.data["hot-water-energy-eff"] == "Very Poor"):
+            self.recommend_tank_insulation(phase=phase)
+            return
+
+    def recommend_tank_insulation(self, phase):
+        """
+        If the home has a very poor hot water system, this is often indicative of a lack of insulation on the hot water
+        tank. This is a very simple and cost effective improvement that can be made to the home.
+        """
+
+        recommendation_cost = self.costs.hot_water_tank_insulation()
+
+        self.recommendations.append(
+            {
+                "phase": phase,
+                "parts": [
+                    # TODO
+                ],
+                "type": "hot_water_tank_insulation",
+                "description": "Insulate the hot water tank with an insulation jacket",
+                "starting_u_value": None,
+                "new_u_value": None,
+                "sap_points": None,
+                **recommendation_cost,
+                "simulation_config": {"hot_water_energy_eff_ending": "Average"}
+            }
+        )
+        return

recommendations/Recommendations.py

@@ -10,6 +10,7 @@ from recommendations.LightingRecommendations import LightingRecommendations
 from recommendations.SolarPvRecommendations import SolarPvRecommendations
 from recommendations.WindowsRecommendations import WindowsRecommendations
 from recommendations.HeatingRecommender import HeatingRecommender
+from recommendations.HotwaterRecommendations import HotwaterRecommendations
 from backend.ml_models.AnnualBillSavings import AnnualBillSavings
 
 
@@ -42,6 +43,7 @@ class Recommendations:
         self.windows_recommender = WindowsRecommendations(property_instance=property_instance, materials=materials)
         self.solar_recommender = SolarPvRecommendations(property_instance=property_instance)
         self.heating_recommender = HeatingRecommender(property_instance=property_instance)
+        self.hotwater_recommender = HotwaterRecommendations(property_instance=property_instance)
 
     def recommend(self):