07f534ee11
Add the system tune-up to the heating Recommendation: keep the existing wet boiler but install better heating controls and fix the cylinder. Two competing Options (the Optimiser picks <=1 across the whole heating rec) per the user's two best control end-states: - system_tune_up — standard controls (programmer + room thermostat + TRVs, SAP 10.2 Table 4e code 2106) - system_tune_up_zoned — time-and-temperature zone control (code 2110, type 3): more SAP uplift for more cost Both keep the boiler (no fuel / SAP code / flue change), set the control ABSOLUTELY to their end-state, and apply the conditional cylinder fixes (an 80 mm jacket when under-insulated, a thermostat when absent — only when a cylinder exists). Each control option is offered only when it genuinely improves the existing control — standard is skipped when the control is already 2106 / 2110 / 2112, zone when already 2110 / 2112 — so neither is ever a downgrade or a no-op. Validated against the Elmhurst "system tune up" re-lodgements (cert 001431): nine befores spanning controls 2101-2113 all converge to the two common afters, proving the control overlay is absolute. The cascade pin is parametrised over two starting controls (2101 "no control" + 2113 "room thermostat and TRVs") x both afters, delta 0 (SAP/CO2/PE). Wires the two MeasureTypes through contingencies (0.15), the offline catalogue (500 / 900), the catalogue-coverage list, the report triggers, and the ARA first-run seed. 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 | ||
| harness | ||
| 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