diff --git a/backend/app/config.py b/backend/app/config.py
index ef48c317..f80da387 100644
--- a/backend/app/config.py
+++ b/backend/app/config.py
@@ -9,9 +9,14 @@ class Settings(BaseSettings):
     ENVIRONMENT: str
     DATA_BUCKET: str
     PLAN_TRIGGER_BUCKET: str
+
+    # Third parties
     EPC_AUTH_TOKEN: str
     ORDNANCE_SURVEY_API_KEY: str
     GOOGLE_SOLAR_API_KEY: str
+    FARADAY_API_KEY: str
+
+    # Database settings
     DB_HOST: str
     DB_PASSWORD: str
     DB_USERNAME: str
diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py
index 00e73b56..8e6bc3b1 100644
--- a/backend/app/plan/router.py
+++ b/backend/app/plan/router.py
@@ -488,6 +488,13 @@ async def trigger_plan(body: PlanTriggerRequest):
                      "carbon_ending"]
         )
 
+        from utils.s3 import save_dataframe_to_s3_parquet
+        save_dataframe_to_s3_parquet(
+            bucket_name="retrofit-datalake-dev",
+            file_key="recommendations_scoring_data_11th_july.parquet",
+            df=recommendations_scoring_data
+        )
+
         model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
 
         all_predictions = model_api.predictions_template()
@@ -503,6 +510,8 @@ async def trigger_plan(body: PlanTriggerRequest):
             for key, scored in predictions_dict.items():
                 all_predictions[key] = pd.concat([all_predictions[key], scored])
 
+        prediction_df = all_predictions["heating_cost_predictions"]
+
         # Insert the predictions into the recommendations and run the optimiser
         # TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
         #       possibility with heating system
diff --git a/etl/bill_savings/data_collection.py b/etl/bill_savings/data_collection.py
index be141c01..35498037 100644
--- a/etl/bill_savings/data_collection.py
+++ b/etl/bill_savings/data_collection.py
@@ -128,12 +128,12 @@ def app():
 
     epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
 
-    sample_size = 100
+    sample_size = 500
 
     energy_consumption_data = []
     for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
         # Skip the first 50
-        if i < 110:
+        if i < 127:
             continue
 
         data = pd.read_csv(directory / "certificates.csv", low_memory=False)
diff --git a/infrastructure/terraform/main.tf b/infrastructure/terraform/main.tf
index b90c73a8..51b4d87d 100644
--- a/infrastructure/terraform/main.tf
+++ b/infrastructure/terraform/main.tf
@@ -220,6 +220,17 @@ module "lambda_hot_water_cost_prediction_ecr" {
   source   = "./modules/ecr"
 }
 
+# For heating and hot water kwh models
+module "lambda_heating_kwh_prediction_ecr" {
+  ecr_name = "heating-kwh-prediction-${var.stage}"
+  source   = "./modules/ecr"
+}
+
+module "lambda_hotwater_kwh_prediction_ecr" {
+  ecr_name = "hotwater-kwh-prediction-${var.stage}"
+  source   = "./modules/ecr"
+}
+
 ##############################################
 # CDN - Cloudfront
 ##############################################