8452cf9e2d
SAP 10.2 Appendix C §C3.2 (PDF p.51), verbatim: "CO2 emissions and
Primary Energy associated with the electricity used for pumping water
through the distribution system are allowed for by adding electrical
energy equal to 1% of the energy required for space and water heating."
Worksheet line (313) = 0.01 × [(307)+(310)]; its CO2 (372) and PE (472)
bill on the Table 12d/12e monthly factors for fuel code 50 ("electricity
for pumping in distribution network"), weighted by the monthly heat
profile per worksheet footnote (a). (307)m/(310)m = (space_demand +
hw_output) / efficiency (the cascade models a heat network's generator
efficiency as 1/DLF).
This un-defers the (372)/(472) front the post-S0380.179 handover flagged
"don't guess until the factor source is identified": the source is
§C3.2 + Table 12d/12e code 50, NOT an empirical constant. The apparent
0.1994/0.2114 "factor" is an Elmhurst DISPLAY artifact — the worksheet
shows the (372) energy column as 0.01×(307) (space only) while computing
emissions on 0.01×(307+310) per the §C3.2 text. Verified EXACT line-by-
line against the CH2 corpus worksheet: (372)=23.6007 CO2 (rating),
(472)=208.2267 PE (demand).
New `_heat_network_distribution_electricity` helper (gated on
`_is_heat_network_main`) precomputes the energy + effective CO2/PE
factors; three new CalculatorInputs fields + calculator.py CO2/PE
summation terms (0.0/None → no-op for individually-heated certs).
Closures:
CH1 (Boilers/Gas) CO2 −23.60→−0.00, PE −208.23→+0.00 — FULLY EXACT
CH3 (HP/Elec) CO2 −98.92→−75.32, PE −457.54→−249.32 (distribution
component closed; code-304 community-HP COP remains)
CH2/CH4/CH6 gain their (372)/(472) component (CO2 +23.6, PE
+208.2); dominant CHP displaced-electricity credit
residual (Table 12f + block 12b/13b) is next slice.
No regression on the other 36 corpus variants (helper returns None off
heat-network mains) + golden + U985 fixtures. 2223 pass + 1 skip + 0
fail; 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