1df83e6f88
SAP 10.2 Appendix N (N3.6 / N3.7(a)) requires PSR-interpolated values
from PCDB Table 362 for any heat-pump cert. The published PCDF Spec
Rev 6b §A.23 documents format 464 for that table; the live
pcdb10.dat (April 2026) ships format 465, which extends 464 with
additional header fields between fields 11 and 12 and a larger PSR
group set. The parser-layer test pins the format-465 offsets against
the BRE web entry for Mitsubishi Ecodan 5.0 kW PUZ-WM50VHA
(pcdb_id=104568, the cohort's dominant heat-pump model — 6 of 7 ASHP
certs use it).
This slice lands only the header fields the downstream APM cascade
needs (PSR-group decoding + linear interpolation follow in slice 102c.2):
field spec ref format-465 idx
brand_name §A.23 field 7 6
model_name §A.23 field 8 7
model_qualifier §A.23 field 9 8
fuel §A.23 field 13 16
service_provision §A.23 field 17 22
hw_vessel_mode §A.23 field 18 23
vessel_volume_l §A.23 field 19 24
vessel_heat_loss_kwh_per_day §A.23 field 20 25
vessel_heat_exchanger_area_m2 §A.23 field 21 26
max_output_kw §A.23 field 30 47
`max_output_kw` is the PSR-denominator per SAP 10.2 PDF p.100 line 5946
("maximum nominal output of the package … divided by the design heat
loss of the dwelling"); BRE labels it "Output power @ -4.7°C" on the
web entry.
Cohort header parse verified end-to-end against BRE web ground truth
for record 104568. Identical field positions apply to the Daikin
EDLQ05CAV3 (102421, cert 9418), confirmed by spot-checking the
populated raw indices.
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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