449d8c5b95
SAP 10.2 Table 3 (PDF p.160) names "Direct-acting electric boiler"
verbatim in the primary-loss zero list (alongside electric immersion,
combi, CPSU, integral-vessel heat pump). RdSAP 10 §12 (p.62) classifies
SAP code 191 as the direct-acting electric boiler. Its cylinder is
immersion-heated with no primary pipework, so no primary circuit loss
applies — but `_primary_loss_applies` had no 191 branch, so a 191 main
(main_heating_category 2, "Boiler and radiators, electric") fell through
to the cat-{1,2} boiler branch and accrued ~1177 kWh/yr of phantom
primary loss on the electric-flat segment.
Validated against the cert-2474 worksheet: §4 (59) primary loss = 0,
(64) HW output 1760 (cylinder) + (64a) shower 581. Cert 2474 HW kWh
3585 → 2408; SAP 64.66 → 70.35 (the residual to the lodged 78 is an
Unknown-meter data-fidelity artifact — the register recorded meter_type=3
"Unknown" but the lodged rating used an 18-hour off-peak meter, per RdSAP
§12 / the example worksheets).
Eval mean|err| 1.720 → 1.708 (headline 45.0%, flat ±1 cert — the
electric-flat segment is dominated by the meter data-fidelity artifact).
Regression green (2448 pass incl. golden 6035 + ASHP cohort 1e-4);
pyright net-zero.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| scripts | ||
| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| 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_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