2be7905637
SAP 10.2 Appendix N3.5 (PDF p.106-107) replaces Table 9c steps 3-4
for heat-pump packages with PCDB data — each month blends the
heating temperature Th, the unimodal (16-hour day, one 8-hour off
period per Table N7 footnote b) zone temperature, and the bimodal
(9-hour day, two off periods per Table N7) zone temperature via
Equation N5:
T = [N24,9 × Th + N16,9 × T_uni + (Nm − N16,9 − N24,9) × T_bi] / Nm
`mean_internal_temperature_monthly` gains an optional
`extended_heating_days_per_month` kwarg (12-tuple of (N24,9_m,
N16,9_m)). When provided, the orchestrator computes T_unimodal per
zone from a single 8-hour off-period reduction and blends; when
None (default — every non-HP cert) it returns T_bimodal directly,
so closed certs (001479, 0330, 9501) are bit-identical.
`cert_to_inputs` derives the per-month tuple for HP certs with PCDB
records carrying `heating_duration_code = "V"` (Variable) — the
only code lodged on modern records per SAP 10.2 PDF p.105 footnote
48. Cohort path: PSR (= max_output_kw × 1000 / (HLC × 24.2 K)) →
Table N5 PSR interpolation → cold-first day allocation. Fixed
durations "24" / "16" / "9" from legacy Table N4 are deferred —
not exercised by the cohort.
Cert 0380 SAP residual closes from +0.5999 → +0.1550 vs worksheet
88.5104. The remaining ~0.16 SAP delta is split between two
orthogonal §5 / §7 residuals (cold-month +0.008°C MIT drift from
spurious HP pump gains; sub-1e-3 efficiency bias) that the next
slices target. Pin tolerance is 1e-2 per month on worksheet (92)
to capture this slice's contract alone, with `feedback_zero_error_
strict` widening documented inline.
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