4d1a58b828
Electric storage heaters (and CPSU) charge overnight and cannot run economically on a single rate, so their presence is physical evidence the dwelling is on an off-peak tariff. RdSAP 10 §12 (PDF p.62) applied Rules 1-4 only for a Dual meter; an "Unknown" (code 3) meter returned STANDARD without consulting the heating type, so a cat-7 storage main billed its overnight charge at the standard 13.19 p/kWh instead of the 7-hour low rate (5.50 p/kWh) — ~2.4x too high → large under-rate. Two coupled fixes: - `rdsap_tariff_for_cert`: for an Unknown meter, infer the off-peak tariff from a Rule-1 CPSU (→10-hour) or Rule-2 storage (→7-hour) main; keep STANDARD otherwise. Direct-acting/room heaters/heat pumps (Rule 3) are NOT off-peak evidence (run on demand, exist on single-rate meters) so they stay STANDARD — billing them 100% at the low rate over-credits. - `_fuel_cost` now resolves its tariff via the §12-aware `_rdsap_tariff` (not the raw `tariff_from_meter_type`), so the off-peak branch fires for these storage certs and the legacy scalar fields bill the low rate. Mirrors `_is_off_peak_meter`'s existing Unknown+electric heuristic (which already routes HW/secondary off-peak), closing the main-space-heating gap. Meter-3 electric cluster: mean |err| 11.18 → 6.52, within-1.0 3 → 5 (cert 7336 -26.1 → -0.16, 0380 -19.9 → +1.0). Eval headline 44.9% → 45.0%, mean |err| 1.82 → 1.76, mean signed -0.08 → +0.02. A few storage certs overshoot (other residuals the standard rate was masking). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
||
|---|---|---|
| .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