b73690fe6e
SAP 10.2 Table 11 allocates a fraction (10-20%) of space heating to a
secondary system based on main heating category. Per Appendix A §A.2.2,
this is applied:
- Always for electric storage heater main systems (codes 401-407, 409,
421); a portable electric heater (code 693) is defaulted when no
secondary is recorded.
- Otherwise only when the cert lodges a secondary_heating_type.
Calculator gains secondary_heating_fraction, secondary_heating_efficiency,
secondary_heating_fuel_cost_gbp_per_kwh on CalculatorInputs and a
secondary_heating_fuel_kwh_per_yr on SapResult. Monthly loop splits
demand: q_main = q_heat × (1 - frac), q_secondary = q_heat × frac, each
converted to fuel via its own efficiency. Cost = main_kwh × main_price
+ secondary_kwh × secondary_price + ... .
Initial implementation applied 10% unconditionally and regressed 300-
cert MAE 5.45 → 6.58 (bias -2.65). Restricted to the conditional rule
above and aggregate returns to flat:
300-cert: MAE 5.45 → 5.43 (flat)
bias +0.22 → -0.52
within ±5: 62.7% → 64.3%
The slice is spec-correct and architecturally enables the secondary-
heating channel; aggregate MAE moves are small because most certs
don't lodge a secondary and most non-storage mains don't force one.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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