cbdee9ec3c
Water heating SAP code 909 (electric instantaneous) and 907 (single-point
gas) heat water at the point of use, serving one outlet with no
distribution pipework. Per SAP 10.2 §4 (p.23, l.1416): "'Single-point'
heaters, which are located at the point of use and serve only one outlet,
do not have distribution losses either." So worksheet (46)m = 0 and the
heat-required line collapses to SAP 10.2 worksheet l.7704
(62)m = 0.85 × (45)m + (46)m + (57)m + (59)m + (61)m
= 0.85 × (45)m (all loss terms zero for a no-cylinder system).
`distribution_loss_monthly_kwh` already supported the
`is_instantaneous_at_point_of_use` flag (and its docstring already named
codes 907/909), but `water_heating_from_cert` hard-coded it to False, so
the cascade applied (46)m = 0.15 × (45)m to single-point heaters. That
0.15 distribution loss exactly cancelled the 0.85 reduction, leaving
(62)m = (45)m. On the cat-10 room-heater fixture (ref 001431, code 909)
that over-stated the water fuel (219) as 2082.6250 instead of the
worksheet's 1770.2313, and inflated the (65)m heat gains (692.47 vs
worksheet 442.55) which in turn suppressed space-heating demand.
Thread the cert's existing instantaneous flag (`_INSTANTANEOUS_WATER_CODES`
= {907, 909}) through `_water_heating_worksheet_and_gains` into both the
demand-pass and final `water_heating_from_cert` calls.
Pins (219) water fuel = 1770.2313 at abs 1e-4 via the extractor → mapper →
rating cascade. §4 suite green (2414 passed, 1 skipped); no existing
fixture exercised the 907/909 path. The residual space-heating fuel gap
((211) 11158.59 vs worksheet 11563.17) this exposes is a separate cause —
next slice.
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