f33557b0e4
SAP 10.2 Appendix N3.6 / N3.7(a) (PDF p.108) compute heat-pump
efficiencies from a PSR-dependent dataset in the PCDB record. Spec PDF
p.100 line 5957 instructs: "The PSR-dependent results applicable to
the dwelling are then obtained by linear interpolation between the two
datasets whose PSRs enclose that of the dwelling."
This slice decodes the format-465 PSR-group block (idx[58] count
followed by N groups × 9 raw fields apiece) and adds the interpolation
primitive. Field positions within each 9-field group reverse-engineered
against Mitsubishi PUZ-WM50VHA (104568) by back-solving cert 0380's
worksheet pin η_space=223.0480, η_water=171.0746:
group offset 0 → PSR
group offset 2 → η_space,1 (% gross)
group offset 6 → η_water,3 (% gross — Appendix N3.7(a) + footnote 49,
PSR-dependent and calculated via the annual performance
method, used directly for HPs providing both space +
water heating)
Offsets 1 / 3 / 4 / 5 / 7 / 8 are unpopulated for record 104568 and
not yet ground-truthed. They likely hold the secondary results
documented under format 464 field 42-43 (specific electricity
consumed, running hours) plus additional format-465 extensions.
The clamping behaviour at the PSR ends is taken from SAP 10.2 PDF
p.101 lines 6007-6008: "if the PSR is greater than the largest PSR in
the database record then the heat pump space and water heating
fractions for the largest PSR should be used, and if the PSR is less
than the smallest PSR in the database record then the heat pump space
and water heating fractions for the smallest PSR should be used".
Verified against cohort:
- Record 104568 (Mitsubishi PUZ-WM50VHA) → 14 PSR groups decoded;
interpolation at PSR=1.43 yields η_space,1≈234.96 and η_water,3
≈285.09, matching back-solved worksheet values (slice 102e applies
the N3.6 ×0.95 and N3.7 ×0.60 in-use factors to close the chain).
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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