e03f08cdc8
The §7 mean-internal-temperature cascade hardcoded the thermal mass parameter
(TMP) to 250 kJ/m²K at all 5 call sites, ignoring construction. RdSAP 10
§5.16 Table 22 (PDF p.48) makes TMP construction-dependent:
100 kJ/m²K — timber frame, cob, park home (regardless of internal
insulation); OR masonry (stone/solid brick/cavity/system
built) WITH internal insulation.
250 kJ/m²K — masonry WITHOUT internal insulation.
A too-high TMP inflates the §7 time constant τ = Cm/(3.6·H) (e.g. 40 h vs
16 h), under-cuts the temperature reduction between heating periods, and
over-states mean internal temperature → over-states space heating.
`_thermal_mass_parameter_kj_per_m2_k(epc)` classifies the MAIN building's
wall via the RdSAP `wall_construction` codes (5/7/8 = timber/cob/park) and
`wall_insulation_type` codes (3/7 = internal); unknown/curtain fall back to
the masonry 250 (no regression on unlisted classes). 17-case parametrised
test covers every Table 22 branch.
Diagnosis (per-line walk vs the user-simulated 001431 worksheet, same
archetype as golden cert 6035): fabric (26-37), internal gains (73), climate
(96)m and HTC (39) all EXACT; the entire +8.78 PE / -1.76 SAP gap was §7 MIT
(92) +0.71 °C, traced to TMP 250 vs Table 22's 100 (solid brick WITH internal
insulation). Fix closes the simulated case to 1e-4 on PE and CO2.
Blast radius: only golden cert 6035 re-pins (solid brick + internal
insulation) — SAP resid -6 → -2, PE +46.42 → +19.16, CO2 +1.07 → +0.42. The
47 dr87 cohort, 6 U985 fixtures and 41-variant heating corpus are all
masonry-no-internal → TMP unchanged at 250, all still pass. 2290 pass
(+17 new), 0 fail; pyright net-zero.
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/heating systems examples | ||
| 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