4291193ba8
SAP 10.2 Table 4f (PDF p.174) row "Warm air heating system fans"
+ footnote e) — verbatim:
Warm air heating system fans e) SFP × 0.4 × V
e) SFP is the specific fan power from the database record for the
warm air unit if applicable; otherwise 1.5 W/(l/s). These values
of SFP include the in-use factor.
If the heating system is a warm air unit and there is balanced
whole house mechanical ventilation, the electricity for warm
air circulation should not be included in addition to the
electricity for mechanical ventilation. However it is included
for a warm air system and MEV or PIV from outside.
V is the volume of the dwelling in m³.
Per Table 4a (PDF p.165-166), warm-air systems are:
- Category 5: heat pumps with warm-air distribution (codes 521,
523, 524 electric; 525, 526, 527 gas-fired)
- Category 9: warm-air systems NOT heat pump (501-511, 520 gas-
fired; 512-514 liquid-fired; 515 Electricaire electric)
Pre-slice the cascade's `_table_4f_additive_components` docstring
explicitly listed "(230b) Warm-air heating fans + (230c) for warm-
air pump" as "Not yet wired" — every Cat 5 / Cat 9 warm-air corpus
variant resolved `pumps_fans_kwh_per_yr` to 0. For electric 2 (code
524 Cat 5 air-source warm-air HP, no MV, V = 227.25 m³), the P960
worksheet block 11a (249) lodges 136.35 kWh × 13.67 p/kWh = £18.64
where the cascade computed 0.
New `_TABLE_4A_WARM_AIR_SAP_CODES` frozenset (22 codes) + leaf helper
`_table_4f_warm_air_heating_fans_kwh(main, dwelling_volume_m3,
has_balanced_mv)` wired at the orchestrator pumps_fans summation
alongside the existing circulation-pump and gas-flue-fan helpers.
Footnote-e balanced-MV omission reads `epc.sap_ventilation.
mechanical_ventilation_kind` via the new
`_has_balanced_mechanical_ventilation` predicate (returns True for
MVHR / MV; False for MEV / PIV / NATURAL).
Per-line walk evidence: cascade `pumps_fans_kwh_per_yr` = 0.0000 vs
worksheet (249) = 136.3500 = 1.5 × 0.4 × 227.25 exactly. Default SFP
from footnote e matches; PCDB warm-air-unit SFP lookup deferred
until a fixture exercises it.
Closures electric 2:
pumps_fans_kwh_per_yr: 0 → 136.35 (EXACT match to worksheet)
ΔSAP +0.7002 → −0.1087 (residual swung past worksheet — the +0.70
pre-slice was an under-counted-fan offset; spec-correct fix lands
just past zero, exposing a small upstream SH cascade gap likely
in the Cat 5 warm-air HP Table 4a SH efficiency or Table 9c MIT
cascade for warm-air mains — follow-up slice)
Δcost −£16.14 → +£2.50
ΔCO2 −2.37 → +16.54 kg
ΔPE −108.58 → +97.69 kWh
No regressions on the other 24 cohort variants — the warm-air-code
gate fires only when `sap_main_heating_code` is in the new frozenset
and only electric 2 has a warm-air SAP code in the corpus. Extended
handover suite: 902 pass / 0 fail (was 901 — +1 from the new AAA
test). Pyright net-zero (43 → 43).
Σ |ΔSAP_c| across the 25-variant cohort: 2.87 → 2.30 (~20%
reduction from this slice).
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 | ||
| 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