fc7c4d2d3b
The SAP/EI rating is computed on UK-average weather (Appendix U Tables U1-U3 region 0) so ratings are nationally comparable, but Appendix U paragraph 1 (PDF p.124) requires that "other calculations (such as for energy use and costs on EPCs) are done using local weather. Weather data for each postcode district are taken from the PCDB". `Sap10Calculator. calculate` ran ONE cascade (UK-average) and fed it to SAP, CO2 AND primary energy, so every cert's EPC-displayed CO2/PE were computed on the wrong climate. Because most of England is warmer than the UK-average, this systematically OVER-counted heating demand on the emissions/PE outputs. The two cascades (`cert_to_inputs` rating, `cert_to_demand_inputs` postcode) already existed; this wires the demand cascade into the production entry point and grafts its CO2/PE onto the rating result (SAP unchanged). The corpus gauge's longstanding +5% CO2/PE over-estimate was mostly this climate bug, NOT (as previously diagnosed) per-cert mapper fidelity: CO2 MAE 0.26 -> 0.12 t/yr (bias +0.18 -> +0.04) PE MAE 13.6 -> 3.8 kWh/m2 (bias +9.0 -> +0.24) SAP within-0.5 = 69.7% (rating cascade, unchanged) Worksheet-validated to 1e-4 on simulated case 45 (heat-pump ground-floor flat, postcode W6): the P960 prints the current dwelling twice — Block 1 on UK-average weather (SAP 60.5318, CO2 692.13) and Block 2 on postcode weather (CO2 626.78, PE 6581.59). Both reproduce exactly. Added a tracked case-45 Summary fixture + two-cascade cascade pin as a permanent guard, and ratcheted the corpus CO2/PE ceilings to 0.13 / 4.2. The e2e Elmhurst suite (Block-1 line refs) now pins the rating cascade directly; the two Vaillant overlay snapshots refreshed to demand-cascade CO2/PE. pyright not installed in this codespace (strict gate not run locally); change is type-trivial (dataclasses.replace over SapResult). Co-Authored-By: Claude Opus 4.8 (1M context) <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 | ||
| harness | ||
| 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 | ||
| next_claude_prompt.txt | ||
| 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