44fb8c0724
SAP 10.2 specification (14-03-2025) §2.6.4 (PDF p.16):
"In the case of decentralised MEV the specific fan power is provided
for each fan and an average value is calculated for the purposes of
the SAP calculations. There are two types of fan, one for kitchens
and one for other wet rooms, and three types of fan location (in
room with ducting, in duct, or through wall with no duct). [...]
The average SFP, including adjustments for the in-use factors, is
given by:
SFPav = Σ(SFP_j × FR_j × IUF_j) / Σ(FR_j) (1)
where the summation is over all the fans, j represents each
individual fan, FR is the flow rate which is 13 l/s for kitchens
and 8 l/s for all other wet rooms, and IUF is the applicable
in-use factor."
And SAP 10.2 §5 Table 4f line (230a):
"Annual electricity for mechanical ventilation fans (kWh/year) =
IUF × SFP × 1.22 × V"
This slice lands the two pure-function cascade primitives:
mev_sfp_av(fan_entries) -> float # equation (1)
mev_decentralised_kwh_per_yr(*, sfp_av, V) -> float # (230a)
`MevFanEntry` carries the per-fan resolved (SFP_w_per_l_per_s, flow_l_
per_s, IUF) triple. Callers (PCDB Table 322 + Table 329 + cert
lodgement of duct type) compose the entries upstream; the cascade
helper does no PCDB resolution itself.
Cert 000565 worksheet line (230a) pinned at 1e-4:
Σ FR = 92.0 l/s (matches worksheet "total flow")
Σ SFP×FR×IUF = 11.7205 W (matches worksheet "total watage")
SFPav = 11.7205 / 92.0 = 0.1274 W/(l/s) ✓ vs ws 0.1274
(230a) = 0.1274 × 1.22 × 820.4385 = 127.5159 ✓ vs ws 127.5159
Pure-function helpers; no cascade integration yet. Next slice
S0380.101 wires HP category mapper; S0380.102 wires cert→inputs
to invoke the cascade. Pyright net-zero per touched file.
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