efa10fe6cb
RdSAP 10 §2 (Ventilation, "Walls" row): "Structural infiltration: 0.25 for steel or timber frame or 0.35 for masonry construction ... System build: treated as masonry." `_is_timber_or_steel_frame` wrongly included wall_construction code 6 (system build) alongside code 5 (timber frame), handing system-build dwellings the 0.25 structural ACH instead of 0.35. On the cat-10 room-heater fixture (ref 001431, walls SY System Build → code 6) this under-stated the infiltration rate (18) by exactly 0.10 (0.45 vs worksheet 0.55), dropping the effective air change (25), the ventilation heat loss (38)m = 0.33 × (25)m × (5), and the heat-transfer coefficient (39) — so space-heating demand (98) came out 404 kWh low ((211) 11158.6 vs worksheet 11563.2). Restrict the 0.25 branch to code 5 only; code 6 (and everything else) is masonry at 0.35. Pins the rating-block (38)m ventilation heat loss mean = 83.3613 W/K at abs 1e-4 and asserts the classifier treats the system-build wall as masonry. §4 suite green (2415 passed, 1 skipped); no existing fixture relied on system-build → 0.25. Residual after this slice: SAP +0.03 / cost -£0.95 — a small fabric (33) gap (-0.15 W/K) plus lighting (232) +1.0 kWh remain as separate causes. 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