4df1ee78b7
The 17a-baseline residuals showed cylinder_insulation_thickness_mm,
cylinder_size and cylinder_insulation_type at ranks 3/6/9 for hot_water_kwh
because the crude 16d formula didn't use them -- the model had to learn
storage physics from raw features.
Now predicted_hot_water_kwh sums:
useful_demand (existing, unchanged)
+ distribution_loss = useful * 0.15
+ storage_loss = volume * insulation_factor * 365 * 0.6
(volume from cylinder_size, factor from
cylinder_insulation_thickness_mm or age-default)
+ primary_circuit_loss = 245 (age A-J) / 60 (age K-M)
- wwhrs_credit = useful * 0.12 if number_baths_wwhrs > 0
- solar_hw_credit = 250 if solar_water_heating
all / efficiency_water = delivered kWh
Same inputs we already extract; just plumbed through. Expected:
predicted_hot_water_kwh feature usage jumps from rank 10 to top tier,
hot_water_kwh MAPE drops from 7.17%, and predicted_ecf gets tighter for
gas-heat + electric-DHW mid-band homes -> SAP MAPE marginally better.
5 new AAA tests; VERSION 2.1.0 -> 2.2.0 (MINOR; column semantics enriched).
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|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs/adr | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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