Added in the new cost apis

backend/Property.py

@@ -357,8 +357,8 @@ class Property:
             simulation_epc = self.epc_record.prepared_epc.copy()
             # Replace the understores with hyphens
             simulation_epc = {k.replace("_", "-"): v for k, v in simulation_epc.items()}
-            simulation_epc.update(simulation_epc)
-            self.simulation_epcs = simulation_epc
+            simulation_epc.update(phase_epc_transformation)
+            self.simulation_epcs[phase] = simulation_epc
 
     @staticmethod
     def create_recommendation_scoring_data(

backend/app/config.py

@@ -8,9 +8,6 @@ class Settings(BaseSettings):
     SECRET_KEY: str
     ENVIRONMENT: str
     DATA_BUCKET: str
-    SAP_PREDICTIONS_BUCKET: str
-    CARBON_PREDICTIONS_BUCKET: str
-    HEAT_PREDICTIONS_BUCKET: str
     PLAN_TRIGGER_BUCKET: str
     EPC_AUTH_TOKEN: str
     ORDNANCE_SURVEY_API_KEY: str
@@ -21,6 +18,14 @@ class Settings(BaseSettings):
     DB_PORT: str
     DB_NAME: str
 
+    # Prediction buckets
+    SAP_PREDICTIONS_BUCKET: str
+    CARBON_PREDICTIONS_BUCKET: str
+    HEAT_PREDICTIONS_BUCKET: str
+    LIGHTING_COST_PREDICTIONS_BUCKET: str
+    HEATING_COST_PREDICTIONS_BUCKET: str
+    HOT_WATER_COST_PREDICTIONS_BUCKET: str
+
     class Config:
         env_file = "backend/.env"
 
@@ -28,3 +33,15 @@ class Settings(BaseSettings):
 @lru_cache()
 def get_settings():
     return Settings()
+
+
+@lru_cache()
+def get_prediction_buckets():
+    return {
+        "sap_change_predictions": get_settings().SAP_PREDICTIONS_BUCKET,
+        "heat_demand_predictions": get_settings().HEAT_PREDICTIONS_BUCKET,
+        "carbon_change_predictions": get_settings().CARBON_PREDICTIONS_BUCKET,
+        "lighting_cost_predictions": get_settings().LIGHTING_COST_PREDICTIONS_BUCKET,
+        "heating_cost_predictions": get_settings().HEATING_COST_PREDICTIONS_BUCKET,
+        "hot_water_cost_predictions": get_settings().HOT_WATER_COST_PREDICTIONS_BUCKET
+    }

backend/app/plan/router.py

@@ -10,7 +10,7 @@ from sqlalchemy.exc import IntegrityError, OperationalError
 from sqlalchemy.orm import sessionmaker
 from starlette.responses import Response
 
-from backend.app.config import get_settings
+from backend.app.config import get_settings, get_prediction_buckets
 from backend.app.db.connection import db_engine
 from backend.app.db.functions.materials_functions import get_materials
 from backend.app.db.functions.portfolio_functions import aggregate_portfolio_recommendations
@@ -462,21 +462,13 @@ async def trigger_plan(body: PlanTriggerRequest):
 
         model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
 
-        all_predictions = {
-            "sap_change_predictions": pd.DataFrame(),
-            "heat_demand_predictions": pd.DataFrame(),
-            "carbon_change_predictions": pd.DataFrame()
-        }
+        all_predictions = model_api.predictions_template()
         to_loop_over = range(0, recommendations_scoring_data.shape[0], SCORING_BATCH_SIZE)
         for chunk in tqdm(to_loop_over, total=len(to_loop_over)):
             predictions_dict = model_api.predict_all(
                 df=recommendations_scoring_data.iloc[chunk:chunk + SCORING_BATCH_SIZE],
                 bucket=get_settings().DATA_BUCKET,
-                prediction_buckets={
-                    "sap_change_predictions": get_settings().SAP_PREDICTIONS_BUCKET,
-                    "heat_demand_predictions": get_settings().HEAT_PREDICTIONS_BUCKET,
-                    "carbon_change_predictions": get_settings().CARBON_PREDICTIONS_BUCKET
-                }
+                prediction_buckets=get_prediction_buckets()
             )
 
             # Append the predictions to the predictions dictionary

backend/ml_models/api.py

@@ -11,13 +11,19 @@ class ModelApi:
     MODEL_PREFIXES = [
         "sap_change_predictions",
         "heat_demand_predictions",
-        "carbon_change_predictions"
+        "carbon_change_predictions",
+        "lighting_cost_predictions",
+        "heating_cost_predictions",
+        "hot_water_cost_predictions",
     ]
 
     MODEL_URLS = {
         "sap_change_predictions": "sapmodel",
         "heat_demand_predictions": "heatmodel",
-        "carbon_change_predictions": "carbonmodel"
+        "carbon_change_predictions": "carbonmodel",
+        "lighting_cost_predictions": "lightingmodel",
+        "heating_cost_predictions": "heatingmodel",
+        "hot_water_cost_predictions": "hotwatermodel",
     }
 
     def __init__(
@@ -39,6 +45,17 @@ class ModelApi:
         self.portfolio_id = portfolio_id
         self.timestamp = timestamp
 
+    @staticmethod
+    def predictions_template():
+        return {
+            "sap_change_predictions": pd.DataFrame(),
+            "heat_demand_predictions": pd.DataFrame(),
+            "carbon_change_predictions": pd.DataFrame(),
+            "lighting_cost_predictions": pd.DataFrame(),
+            "heating_cost_predictions": pd.DataFrame(),
+            "hot_water_cost_predictions": pd.DataFrame(),
+        }
+
     def upload_scoring_data(self, df: pd.DataFrame, bucket: str, model_prefix: str) -> str:
         """
         The sap model api needs a scoring data that is sitting in s3 to use as a dataset to score on

etl/bill_savings/data_collection.py

@@ -133,7 +133,7 @@ def app():
     energy_consumption_data = []
     for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
         # Skip the first 50
-        if i < 26:
+        if i < 110:
             continue
 
         data = pd.read_csv(directory / "certificates.csv", low_memory=False)