cb089a630c
An instantaneous point-of-use electric water heater (WHC 907/909, no cylinder) on an off-peak tariff was billed 100% at the off-peak LOW rate by the generic electric-off-peak else-branch. But SAP 10.2 §12 (PDF line 2680) computes the off-peak on-peak HW proportion via Table 13, "dependent on the total floor area and the CYLINDER size" — it presumes a stored-water cylinder charged overnight. An instantaneous heater has NO cylinder, heats on demand, and cannot shift to the off-peak window, so 100% of its consumption bills at the HIGH rate (Table 12a WH high-rate fraction 1.0). Both the SAP-cost rate (`_hot_water_fuel_cost_gbp_per_kwh`) and the ADR-0014 / CO2-PE fraction (`_hot_water_high_rate_fraction`) fixed consistently (low-rate scalar -> 7-hour high rate; fraction 0.0 -> 1.0). Localised by deep-diving corpus cert 74061136 (HHR-storage mid-floor flat, WHC 909): PE matched lodged (+1.6, roof+floor zero-loss) while SAP over-rated +7.72 — the cost-only signature. Its DHW was ours £59 vs lodged £344 (5.8x); the tariff was the whole gap. Fix: +7.72 -> -1.25 (residual is separate small fabric). 7 corpus certs carry electric-instantaneous DHW on off-peak; the 3 outside 0.5 all move sharply inward (MAE the win, not within-0.5 crossings). RdSAP 10 §12 tariff routing confirmed spec-correct (Unknown meter + storage 409 -> off-peak 7-hour, Rule 2) — the bug was the DHW rate, not the tariff. Unit-pinned in test_cert_to_inputs; RealCertExpectation 74061136 = 72. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .claude/skills | ||
| .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 | ||
| modelling_audit.md | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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