7a8c8facec
For any cert lodging a Table 362 heat-pump PCDB record, the cascade now replaces the Table 4a category defaults with PSR-interpolated efficiencies per SAP 10.2 Appendix N (PDF p.108): (206) = 0.95 × η_space,1_interp (N3.6 in-use factor) (217) = in_use_factor × η_water,3_interp (N3.7(a) + footnote 49) where η_space,1 and η_water,3 are PSR-dependent values from the PCDB record's PSR-group table (decoded in slice 102c.2), and the dwelling's PSR is computed per PDF p.100 line 5946-5950: PSR = max_nominal_output_kw / (HLC_annual_avg_W_per_K × 24.2 K / 1000) The N3.7 in-use factor (PDF p.6097) tests three cylinder criteria: 1. cert volume ≥ PCDB volume 2. cert heat-exchanger area ≥ PCDB area (unless PCDB area = 0 per fn53) 3. cert heat loss [(47)×(51)×(52)] ≤ PCDB heat loss All three pass → 0.95; any criterion fails or is unknown → 0.60. The Open EPC API never lodges cylinder heat-exchanger area, so for the cohort this criterion is always "unknown" → in_use_factor = 0.60. Cert 0380 (Mitsubishi ASHP PCDB 104568, ASHP main, 160 L cylinder): cascade PSR = 4.39 / (127.158 × 24.2 / 1000) ≈ 1.4266 cascade η_space,1_interp ≈ 235.24 (PSR-1.2 row 253.9, PSR-1.5 229.2) cascade η_water,3_interp ≈ 285.13 (PSR-1.2 row 287.7, PSR-1.5 284.3) cascade main_heating_eff ≈ 2.2348 (vs worksheet 2.2305, 1.9e-3 diff) cascade HW kWh/yr ≈ 878.05 (vs worksheet 877.97, 0.08 kWh/yr) cascade SAP rating ≈ 89.11 (vs worksheet 88.5104, +0.60) The remaining +0.60 SAP residual is bounded by the ~0.4% PSR-formula drift (the cascade computes PSR=1.4266 from (39)_annual_avg × 24.2 K whereas the worksheet back-solves to ≈ 1.4321). Slice 102f decides whether further PSR refinement is needed to reach a 1e-4 SAP pin. |
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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