452749091d
Per the RdSAP 21 schema in [datatypes/epc/domain/epc_codes.csv][1], the
`glazing_type` enum extends to 15 codes; the legacy SAP 10.2 Table 6b
cascade lookups in `internal_gains.py:106` and `solar_gains.py:178`
only knew codes 1-7. Every API-path cert in the cohort lodges
`glazing_type` via the RdSAP 21 numbering, and triple-glazed
lodgements surface as **code 14** ("triple glazing, installed 2022+").
Pre-slice the cascade fell through to the 0.80 / 0.76 double-glazed
defaults for codes 8-15:
Internal gains g_L (Table 6b):
code 14 → default 0.80 (DG) vs spec 0.70 (TG)
→ daylight factor over-bonused → lighting kWh under-counted
Solar gains g⊥ (Table 6b):
code 14 → default 0.76 (DG) vs spec 0.68 (TG)
→ solar gains over-counted
For cert 0350-2968-2650-2796-5255 (semi-detached, 9 triple-glazed
windows lodged as code 14), this drove:
lighting_kwh_per_yr: cascade 221.79 vs Summary-path 228.44
(-6.65 kWh/yr — daylight bonus too generous → lighting too low)
space_heating_kwh_per_yr: cascade 7000.21 vs Summary-path 6996.94
(+3.28 kWh/yr — extra solar gains lower HP demand)
net ECF: -0.0022 vs Summary-path → SAP +0.031
Same mechanism on the other 5 cohort-1 ASHP API certs.
Fix: extend both lookup tables with the RdSAP 21 additions per the
schema CSV semantics:
| code | description (RdSAP 21) | g_L | g⊥ |
|------|----------------------------------|------|------|
| 8 | triple glazing, known data | 0.70 | 0.68 |
| 9 | triple glazing, 2002-2022 | 0.70 | 0.68 |
| 10 | triple glazing, pre-2002 | 0.70 | 0.68 |
| 11 | secondary glazing, normal-E | 0.80 | 0.76 |
| 12 | secondary glazing, low-E | 0.80 | 0.76 |
| 13 | double glazing, 2022+ | 0.80 | 0.76 |
| 14 | triple glazing, 2022+ | 0.70 | 0.68 |
| 15 | single glazing, known data | 0.90 | 0.85 |
Solar gains also adds code 7 (double known data) for
`_G_PERPENDICULAR_BY_GLAZING_TYPE` to align with the existing
`_G_LIGHT_BY_GLAZING_CODE` code-7 entry (which already mapped to
0.80 = double).
Outcome — Cohort-1 ASHP cohort API path:
cert 0380: +0.025 → +1e-6 (close to exact)
cert 0350: +0.031 → +2.2e-5 (close to exact)
cert 2225: +0.029 → -4.8e-5 (close to exact)
cert 2636: +0.015 → -0.015 (sign flip; cantilever-specific
residual surfaces; same |Δ| as Summary)
cert 3800: +0.023 → -2e-5 (close to exact)
cert 9285: +0.029 → -3.4e-5 (close to exact)
5 of 6 API path certs now sit at <1e-4 vs worksheet. Cert 2636
matches its Summary-path residual (-0.015) — the cantilever fixture
has its own non-glazing residual to be diagnosed separately.
Cohort-2 Summary path unchanged (33 exact + 5 ≤0.07) — the cohort-2
certs lodge glazing codes 1-7 (RdSAP 17 numbering still surfaces in
Elmhurst Summary PDF lookups), so codes 8-15 only affect the
RdSAP-21-schema API path.
Golden API fixture pins updated to reflect the tightened cascade-vs-API
alignment (7 certs: 0380, 0350, 2225, 2636, 3800, 9285, 9418). SAP
integer residuals unchanged (all sit at +0).
Pyright net-zero on touched files (22 → 22).
Tests: 710 → **711** pass (+1 new: cert 0350 fixture-shape test for
glazing_type=14 routing to g⊥=0.68 with `total_solar_gains_monthly_w[0]
≈ 67.00 W` (vs pre-slice 74.88 W at the DG default), proving code 14
hits the triple-glazed Table 6b row.) 10 expected fails unchanged.
[1]: datatypes/epc/domain/epc_codes.csv (RdSAP-Schema-21.0.1).
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