1dcbdb28e6
Appendix J equations J1–J3. Per-day hot water draw for mixer showers combines the per-day shower count (rising with N, depressed slightly when a bath is also present) with each outlet's flow × 6 min × Table J5 behavioural factor, then multiplied by the cold-water-dependent hot fraction (41 °C delivery vs 52 °C hot supply, Tcold from J1). Multi-outlet handling: N_shower is split across outlets so a dwelling with two identical mixers produces the same (42a)m total as a single outlet — the count only matters when outlets have different flow rates. Instantaneous electric showers belong in (64a)m and must be excluded from the input. Validated against the Elmhurst non-RR fixtures (both 1 vented mixer at 7 L/min, mains Tcold): - 000490 N=2.1468 → Jan V_d,hot = 52.6878 - 000474 N=1.8896 → Jan V_d,hot = 48.9139 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
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| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
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| AGENTS.md | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
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| Makefile | ||
| MEMORY.md | ||
| package-lock.json | ||
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| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_backlog.sh | ||
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| test.requirements.txt | ||
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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