6636f1c333
SAP 10.2 §9.4.11 (PDF p.30) "Boiler interlock":
For the purposes of the SAP, an interlocked system is one in which
both the space and stored water heating are interlocked. If either
is not, the 5% seasonal efficiency reduction is applied to both
space and water heating; if both are interlocked no reductions are
made.
Table 4c (PDF p.169-170) lodges -5 for both Space and DHW columns on
the "No boiler interlock — regular boiler" row. Pre-slice the cascade
applied the -5pp adjustment ONLY to the `water_eff` scalar fallback
(`cert_to_inputs.py:4354`) and missed:
(a) the SH efficiency path (cascade kept the raw PCDB winter eff for
space heating);
(b) the PCDB Equation D1 monthly cascade (Eq D1 received raw
winter/summer values without the -5pp adjustment).
RdSAP §3 (PDF p.57) defines boiler interlock as "Assumed present if
there is a room thermostat and (for stored hot water systems heated
by the boiler) a cylinder thermostat. Otherwise not interlocked."
Cert pcdb 1 (Potterton KOA PCDB 716 + 110 L cylinder + Cylinder Stat:
No) reproduces the pattern: worksheet (210) = 60% = PCDB winter
65 - 5; worksheet (217)m monthly Eq D1 pivots on (winter 60,
summer 48) not (65, 53).
The SH path is further gated on `pcdb_main is not None` because
§9.4.11 only applies to "gas and liquid fuel boilers" — cert 000565
(ASHP Main 1) keeps its raw SH eff. The combi-fed-cylinder DHW path
(cert 000565 WHC 914 to PCDB combi Main 2) continues to receive its
existing -5pp via the `water_pcdb_main` gate (unchanged).
Corpus impact: pcdb 1 SAP residual +6.95 → +3.40; cost -£157.61 →
-£75.68; CO2 -845.81 → -397.02; PE -3135.30 → -1601.74. No other
variant has PCDB main + cylinder + no thermostat, so the other 24
corpus pins are unchanged.
Extended handover suite: 884 pass, 0 fail (was 883 + 1 new AAA test
pinning the §9.4.11 SH eff path).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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|---|---|---|
| .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