474052d303
Per SAP 10.2 spec page 171 Table 4e "Heating system controls" — boiler
systems with radiators (Group 1):
2110: "Time and temperature zone control by arrangement of plumbing
and electrical services" → type 3
2111: "TRVs and bypass" → type 2
2112: "Time and temperature zone control by device in PCDB" → type 3
2113: "Room thermostat and TRVs" → type 2
`_CONTROL_TYPE_BY_CODE` previously bucketed 2111 + 2113 with the type 3
codes, but neither lodges any time-zone control — they're TRV-class
controls (closer to programmer + room thermostat). The misclassification
propagated through SAP 10.2 Table 9 to swap the elsewhere-zone
off-period pattern from (7, 8) to (9, 8) — i.e. the spec's "heating
0700-0900 and 1800-2300" pattern (footnote b) instead of "heating
0700-0900 and 1600-2300" (footnote a). Under-counted MIT by ~0.67 °C
across the year, dropping space-heating demand and over-predicting SAP:
- cert 0652-3022-1205-2826-1200: +1.93 → -1e-5
- cert 6835-3920-2509-0933-5226: +0.72 → +0.015
Cohort-2 outcome (38 certs, Summary path):
exact (<1e-4): 21 → **22** (+1: cert 0652 closes)
≤±0.07: 13 → **14** (+1: cert 6835 moves from ±0.5..1)
±0.5..1: 2 → **1** (-1: cert 6835 closes out)
±1..5: 1 → **0** (-1: cert 0652 closes out)
No cohort-1 regressions (all certs there use codes 2106 / 2206;
neither uses 2111/2113).
Pyright net-zero (cert_to_inputs.py 35→35, test 13→13).
Tests: 704 pass (existing control-type test extended; +2 new
assertions for codes 2111/2113), 10 expected fails unchanged.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|---|---|---|
| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| 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