aa2c7a9171
SAP 10.3 §12 charges fuel costs by end-use, not by main heating fuel.
For a gas-heated dwelling with an electric immersion hot-water cylinder,
HW bills at the electric rate (13.19 p/kWh) not the gas main-heating
rate (3.48 p/kWh) — a 3.8× cost difference for HW that propagates
straight to ECF. Lighting, central-heating pumps, and fans always
electric regardless of main fuel.
Discovered by hand-tracing cert 8035-9023 (Detached bungalow, actual
SAP 43, predicted 63). Trace showed our hot-water + lighting + pumps
lines were charging mains-gas rates throughout, under-counting cost by
~£290/yr.
100-cert parity probe (biggest single Session-B slice so far):
MAE 5.70 → 4.90 (-0.80, -14%)
RMSE 7.48 → 6.68 (-11%)
within ±1: 20% → 24%
within ±3: 37% → 46%
within ±5: 54% → 67%
bias +1.50 → -1.44 (over-corrected by ~3 SAP points)
The over-correction (bias now slightly negative) means we're now
under-predicting on average. Next slice tackles where we're charging
too much electricity — probably HW on dwellings with combi boilers (no
immersion, water still on main fuel) and the water_heating_code 901
("from main system") inheritance path.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
| Dockerfile.test.dockerignore | ||
| Makefile | ||
| MEMORY.md | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_backlog.sh | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| test.requirements.txt | ||
| tox.ini | ||
| UBIQUITOUS_LANGUAGE.md | ||
Model Repository
This repository contains the code pertaining to the development of the data science and machine learning products being utilised by Hestia.
The different folders in this repository relate to services that can be used independently, or can be imported and used as part of a larger application
Getting Started
Prerequisites
Dev Container Setup
This repo uses a Docker Compose-based dev container. The model-backend service joins a shared-dev Docker network so it can communicate with other local services (e.g. a frontend container) running on your machine.
VS Code users: The initializeCommand in devcontainer.json creates the shared-dev network automatically before the container starts. No manual step required — just open the repo and select Reopen in Container.
Non-VS Code / CI workflows: Run the following once before starting the container:
make dev-setup
This is idempotent and safe to re-run if the network already exists.
Folders
backend/
This folder contains the code for the fastapi backend service, which provides an interface to much of the functionality in this repository, for the frontend
model_data/
This folder contains related to the reading and preparation of assessment model data, including pulling out epc attributes
Testing
All tests can be run, against the configuration in pytest.ini running
pytest
This will run the complete panel of tests and report on coverage in the locations specified by the pytest.ini file.
To run tests in a specific service, e.g. inside of model_data, simply run
pytest --cov-config=model_data/.coveragerc --cov=model_data
This will produce the test results and coverage reports