3581513b7e
The §5 (70) internal-gains mirror of S0380.201's Table 4f (230c). SAP 10.2 Table 5a note a) (PDF p.177) verbatim: "Where there are two main heating systems serving different parts of the dwelling, assume each has its own circulation pump and therefore include two figures from this table. ... Where two main systems serve the same space a single pump is assumed." Simulated case 6 (dual oil, 51% radiators + 49% underfloor) lodges Main 1 "2013 or later" (3 W) + Main 2 unknown date (7 W) → worksheet (70) = 10 W in the 8 heating months. The cascade billed a single Main 1 pump (3 W). New `_second_main_central_heating_pump_gain_w` adds the second main's gain (at its own pump-age bucket), gated on a lodged main_heating_fraction > 0 — the same genuine-second-space-heating-main test as S0380.201, so DHW-only second mains (cert 000565 Main 2 combi via WHC 914, fraction 0) keep a single pump (70)=3. Refactored the per-detail pump predicate (`_main_detail_has_central_heating_pump`) and date bucket (`_pump_date_category_for_detail`) out of the orchestrator. Re-pin: golden 0240 (dual-main oil combi, both unknown date) (70) 7 → 14 W; the extra internal gain lowers space-heating demand → SAP cont 72.18 → 72.24 (integer 72 unchanged), PE +2.8092 → +2.5812, CO2 +0.1385 → +0.1269 (both closer to zero). Validated against the case-6 worksheet. This closes the (70) leg of case 6's space-demand gap. Remaining for full case-6 closure: roof fabric (37) +1.176 W/K (room-in-roof shell) and HW (216) Eq-D1 water efficiency −1.6%. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| 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