b54fda952f
S0380.66 landed (H23) with an incorrect unit treatment: the spec
formula on SAP 10.2 p.76 is
Y_HW = [(H18)m × (H6) × (H5) × (H9)m × (41)m × 24] ÷ [1000 × (H17)m]
and Appendix U (per `domain/sap10_calculator/climate/appendix_u.
horizontal_solar_irradiance_w_per_m2`) returns (H7)m as a monthly-
average flux in W/m². That makes (H9)m = (H1) × (H2) × (H7)m × (H8)
an instantaneous power in W — the `× hours × 24 / 1000` factor in
the (H23) formula is what time-integrates W·h → kWh so the Y_HW
ratio lands dimensionless against (H17)m (kWh/month).
S0380.66's (H23) elided the time integration by absorbing it into
the input parameter (a kWh/m²/month name) — that broke unit
consistency with the downstream Appendix U integration this module
will consume in the next slice.
Changes:
- New `monthly_solar_energy_available_h9_w` — pure (H9)m calculator
taking aperture, η₀, (H7)m flux tuple, and overshading. Returns W.
- `hot_water_factor_y_monthly_h23`: parameter renamed
`monthly_solar_energy_available_h9_w` (was `..._kwh_per_m2`); new
`hours_in_month` parameter; formula now includes the spec's
`× hours / 1000` time integration explicitly.
Tests:
- `test_monthly_solar_energy_available_h9_applies_spec_formula` —
cert 000565 H1/H2/H8 with flat 100 W/m² flux → 192 W (the spec
multiplicand 3 × 0.8 × 100 × 0.8).
- `test_hot_water_factor_y_h23_applies_w_to_kwh_time_integration` —
unit-consistency pin: H9=1000 W, hours=744, H17=744 kWh → Y=1.0.
- `test_hot_water_factor_y_h23_clamps_lower_bound_at_zero` updated
to the new parameter name and supplies `hours_in_month`.
Test suite: 277 pass + 9 expected 000565 cascade-gap fails. Pyright
net-zero on both touched files.
Spec source: SAP 10.2 specification (14-03-2025) Appendix H p.76.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| 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