65e2025f96
The published RdSAP 10 Specification 10-06-2025 PDF Table 32 (p.95)
lists heating oil at 7.64 p/kWh. Two independent operational sources
both use 5.44 p/kWh for the same fuel:
- Elmhurst P960 worksheets across all five oil-fired variants in
`sap worksheets/heating systems examples/` (oil 1, oil pcdb 1/2/3,
pcdb 1) lodge 5.4400 p/kWh on (240) "Space heating - main system 1"
and (247) "Water heating (other fuel)" for every "FuelType: Heating
oil" worksheet.
- The gov.uk EPC register's lodging software back-solves to ~5.48
p/kWh from cert 0240-0200-5706-2365-8010's lodged SAP 73 (oil + PV
detached, age J). With heating-oil at 5.44 in the cascade this cert
closes to ΔSAP = 0 exactly against its lodged value.
The BRE technical papers (`docs/specs/sap10 technical papers/`) carry
no Table 32 errata or fuel-price update, so the change is grounded in
empirical cross-source evidence rather than a spec citation — the
worksheet PDF is the source of truth per the project convention.
Post-flip residuals:
Heating-systems corpus (cascade − worksheet ΔSAP_c):
oil 1 −9.7030 → +2.6578
oil pcdb 1 −11.6343 → +0.4239 ← within 1 SAP of closure
oil pcdb 2 −11.6343 → +0.4239
oil pcdb 3 −10.8674 → +1.1597
pcdb 1 −9.4083 → +6.9521 ← largest remaining oil-cohort gap
Golden fixtures (cascade − lodged SAP):
0240-0200-5706-2365-8010 resid −10 → +0 ← EXACT closure
0390-2954-3640-2196-4175 resid −6 → +7 ← oil-price bug was
masking +13 SAP of
opposite-direction
cascade gaps; now
exposed for follow-up
PE / CO2 residuals are unaffected by the unit-price flip (cost-only
change). The 41-variant corpus regression guard (S0380.129) holds; all
other golden cohorts pass unchanged. Extended handover suite: 874 pass.
Bio-FAME (code 73) shows the inverse divergence on oil 3/4 worksheets
(worksheet 7.64 vs spec 5.44 — possible row-swap typo in the spec PDF)
but flipping it has no measurable cascade effect today, so deferred
until a cert that exercises it surfaces.
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