bd193e06fc
SAP 10.2 Table 3 (PDF p.160) "Primary circuit loss":
"Primary circuit loss applies when hot water is heated by a heat
generator (e.g. boiler) connected to a hot water storage vessel via
insulated or uninsulated pipes (the primary pipework). Primary loss
is set to zero for the following:
Electric immersion heater
Combi boiler ...
CPSU ..."
A Table 4b regular (non-combi, non-CPSU) gas or liquid-fuel boiler
feeding a cylinder is in neither zero-loss list, so primary loss must
apply. Pre-slice the Elmhurst-path fallback in `_primary_loss_applies`
only covered PCDB Table 322 records (S0380.142) — when the cert lodges
a Table 4b code (e.g. oil 1 sap_main_heating_code 127 "Condensing oil
boiler") with no PCDB index and no `main_heating_category` lodgement,
primary loss silently fell through to zero.
This slice extends the Elmhurst-path fallback in `_primary_loss_applies`
to fire when `sap_main_heating_code` is in the Table 4b code range
(101-141) and NOT in the combi/CPSU sub-row exclusion set per Table 3:
Combi codes: 103, 104, 107, 108, 112, 113, 118, 128, 129, 130
CPSU codes: 120, 121, 122, 123
Oil 1 worksheet (59)m daily rate = 1.3972 kWh/day uniform = 14 ×
[0.0245 × 3 + 0.0263] (uninsulated pipework, has cylinder thermostat +
separately timed DHW → h=3 winter & summer per Table 3 split). Annual
sum = 365 × 1.3972 ≈ 510 kWh/yr — matches the worksheet's (59) annual.
Cascade impact on heating-systems corpus:
- oil 1 SAP residual +2.66 → +1.76 (Δ -0.90)
cost -£61.24 → -£40.60 (Δ +£20.64)
CO2 -242.27 → -129.22 (Δ +113.05 kg/yr)
PE -1050.49 → -590.02 (Δ +460.47 kWh/yr)
Only the oil 1 variant moves — every other cascade-OK variant either
already routes primary loss via the PCDB Table 322 branch (oil pcdb 1/
2/3, pcdb 1) or via the boiler-category {1,2} branch. The other oil
codes 124/125/126/131/132 + range-cooker codes 133-141 are gated for
free by the same dispatch when their certs surface in future cohorts.
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