Merge pull request #621 from Hestia-Homes/main

Upgrading rds storage type to gp3 to improve throttling issues

backend/Property.py

@@ -22,7 +22,6 @@ from recommendations.recommendation_utils import (
 )
 from backend.ml_models.AnnualBillSavings import AnnualBillSavings
 from backend.app.utils import sap_to_epc
-from backend.Funding import Funding
 import backend.app.assumptions as assumptions
 
 ENVIRONMENT = os.environ.get("ENVIRONMENT", "dev")
@@ -340,7 +339,6 @@ class Property:
             recommendation_record=recommendation_record,
             recommendations=property_representative_recommendations,
             primary_recommendation_id=self.id,
-            non_invasive_recommendations=self.non_invasive_recommendations,
         )
 
         return scoring_dict
@@ -1243,7 +1241,8 @@ class Property:
             "heat network": "Natural Gas (Community Scheme)",
             "lpg": 'LPG',
             "biodiesel": "Smokeless Fuel",
-            "b30d": "B30K Biofuel"
+            "b30d": "B30K Biofuel",
+            "coal": "Coal",
         }
 
         self.heating_energy_source = list({

backend/engine/engine.py

@@ -15,7 +15,6 @@ from sqlmodel import Session
 
 from etl.epc.Record import EPCRecord
 from sqlalchemy.exc import IntegrityError, OperationalError
-from sqlalchemy.orm import sessionmaker
 from starlette.responses import Response
 from backend.app.BatterySapScorer import BatterySAPScorer
 

etl/customers/peabody/Nov 2025 Consulting Project/d_restart_failed_subtasks.py

@@ -0,0 +1,12 @@
+"""
+For the Peabody project, there were a number of subtasks that failed due to issues, with the most
+prominent being errors with the property address and ID data.
+
+This script will fetch those failed subtasks, get the associated properties and delete the properties
+from the database so that the subtasks can be restarted cleanly.
+
+Additionally, we wil find the problematic records and remove them
+
+Given we ran an EPC C scenario, we should check how many properties, below EPC C we have, that have no plan
+or recommendations in case something went wrong
+"""

etl/epc/Dataset.py

@@ -697,8 +697,12 @@ class TrainingDataset(BaseDataset):
                 for x in missing_descriptions:
                     desc_cleaner = cleaner(x)
                     cleaned = desc_cleaner.process()
-                    # IF NODATA, REMAP TO NONE VALUES
+                    # IF NODATA, REMAP TO NONE VALUES, apart from walls which we want to keep as is
-                    if all((pd.DataFrame(cleaned, index=[0]).T)[0] == False):
+                    # If we convert the walls data to None, we end up converting booleans to None which
+                    # causes issues downstream
+                    if all(
+                        (pd.DataFrame(cleaned, index=[0]).T)[0] == False
+                    ) and component != "walls":
                         cleaned = {key: None for key in cleaned.keys()}
                     cleaned_data.append(
                         {

infrastructure/terraform/main.tf

@@ -85,6 +85,10 @@ resource "aws_db_instance" "default" {
   ca_cert_identifier = "rds-ca-rsa2048-g1"
   # Temporary to enfore immediate change
   apply_immediately = true
+  # Set up storage type to gp3 for better performance
+  storage_type      = "gp3"
+  iops              = 3000
+  storage_throughput = 125
 }
 
 # Set up the bucket that recieve the csv uploads of epc to be retrofit

recommendations/Recommendations.py

@@ -698,6 +698,13 @@ class Recommendations:
                     "heating_cop": 0.85,
                     "hotwater_cop": 0.85
                 }
+            if main_fuel_description in ['coal (community)']:
+                return {
+                    "heating_fuel_type": "Coal",
+                    "hotwater_fuel_type": "Coal",
+                    "heating_cop": 0.85,
+                    "hotwater_cop": 0.85
+                }
             logger.warning(
                 "Unhandled community fuel."
                 f"Fuel: {main_fuel_description}"