803da062a2
SAP 10.2 §10b: hot water for a community-heating dwelling bills at the
heat-network rate, not the cert-lodged fuel. Elmhurst §15.0 lodges
`water_heating_fuel_type = "Mains gas"` (3.48 p/kWh) as a placeholder on
community certs; the worksheet (342) Water-heating cost = (310) × the
S0380.171 CHP heat-fraction blend — the SAME rate as space heating (340).
Per-line walk of the CH2 block 10b:
(340) space = 11837.83 × 0.037955 = 449.3047 (cascade EXACT)
(342) water = 3854.12 × 0.037955 = 146.2830 (cascade billed
3854.12 × 0.0348 = 134.12 → −£12.16, the whole residual)
(350) lighting + (351) standing → (355) 754.1502.
`_hot_water_fuel_cost_gbp_per_kwh`'s `inherit_main_for_community_heating`
path already routes HW cost through `_fuel_cost_gbp_per_kwh(main)` (the
CHP blend), but its gate `_is_community_heating_hw_from_main` excluded
code 302. S0380.182 wired the 302 CO2/PE credit via
`_heat_network_code_302_effective_factor`, which intercepts the HW
CO2/PE helpers ABOVE this predicate's branch — so extending the
predicate to include 302 now affects ONLY the cost path.
Closures:
CH2 (CHP/Gas) SAP +0.5277→−0.0000, cost −£12.16→−£0.00 — FULLY EXACT
CH4 (CHP/Oil) SAP +0.5277→−0.0000, cost −£12.16→−£0.00 — FULLY EXACT
CH6 (CHP/Coal) SAP −7.49→−8.02, cost +£172.68→+£184.84 — its HW now
also bills the blend, compounding the DLF=1.0 quirk
(cascade DLF=1.45); same separate CH6 DLF front.
Corpus now 39 variants EXACT on all four metrics (CH2/CH4 join). Open:
CH3 CO2/PE (code-304 community-HP COP), CH6 all-metric (DLF=1.0 manual
override the Summary doesn't carry). 2225 pass + 1 skip + 0 fail
(tolerances 1e-4 all metrics); pyright net-zero 32→32.
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/heating systems examples | ||
| 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