18a337b3a1
SAP 10.2 worksheet block 12b/13b (367)/(467) for a community heating
electric heat pump (Table 4a code 304 → Table 12 fuel 41 "heat from
electric heat pump"). The HP meters grid electricity, so per Table 12
note (s)/(t) + block 12b/13b footnote (a) its emission/PE factor is the
MONTHLY Table 12d/12e cascade (fuel 41 = standard-electricity profile),
weighted by the network heat profile, then × 1/heat-source-eff (1/COP):
(367)/(467) = [(307)+(310)] / COP × Σ((307+310)_m × factor_m)/Σ(...)
Per-line walk of CH3 (the displayed (367) 0.1535 / (467) 1.5717 are PDF
artifacts; the (373)/(473) totals reconcile only with):
CO2 factor = 0.15040 (monthly Table 12d wtd) vs cascade annual 0.136
PE factor = 1.55692 (monthly Table 12e wtd) vs cascade annual 1.501
Pre-slice the cascade routed code 304 through the non-electric branch
(`_co2_factor_kg_per_kwh(main) × 1/COP` = annual × scaling). New
`_is_heat_network_electric_main` (heat-network main whose fuel has a
Table 12d monthly set — i.e. fuel 41) routes all four factor helpers
(main + HW, CO2 + PE) through the monthly cascade × 1/COP. Non-electric
heat networks (gas 51 / oil 53 / coal 54) have no monthly set → annual
path unchanged (CH1, CH6 untouched).
Closure (CH3 was already SAP+cost EXACT):
CH3 (HP/Elec) CO2 −75.32→+0.0000 (= [(307+310)/3]×(0.1504−0.136)),
PE −249.32→−0.0000 (× (1.5569−1.501)) — FULLY EXACT
Corpus now 40/41 EXACT on all four metrics. Only CH6 remains: its
worksheet lodges a manual DLF=1.0 ("two adjoining dwellings") absent
from the Summary PDF (byte-identical to CH4 bar fuel type) — an
architectural limit, not a cascade gap. 2226 pass + 1 skip + 0 fail
(tolerances 1e-4 all metrics); pyright net-zero 43→43.
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