dfcd7af57c
SAP 10.2 Table 4c(3) (PDF p.169) "Factor for controls and charging method" multiplies a heat network's heat requirement by 1.05-1.10 for FLAT-RATE charging (note d: household pays a fixed amount regardless of heat used, so no incentive to economise), and by 1.0 for charging linked to use. The worksheet folds it into the heat-network requirement alongside the Table 12c distribution loss factor: (307) space = (98c) x (302) x (305) x (306) (310) DHW = (64) x (305a) x (306) Our cascade applied (306) DLF but never (305)/(305a), so every flat-rate community-heating cert under-counted demand -> over-rated SAP. Folded the factor into the 1/DLF efficiency override at the space-heating (206) and DHW (water-inherits-from-main) sites. Space column adds +0.05 for no thermostatic control (2301/2302); DHW column is 1.05 flat-rate / 1.0 linked-to-use. Corpus (RdSAP-21.0.1, 1000 certs): community cluster median +0.32 -> -0.19, within-0.5 38% -> 62% (control 2307 +0.83 -> -0.19; 2306 unchanged at factor 1.0 as spec requires). Overall gauge 65.0% -> 65.9%, MAE 1.174 -> 1.160. Ratcheted the corpus-test floor 0.62 -> 0.63 / MAE ceiling 1.25 -> 1.22. Also records (corpus-test comment + scripts/decompose_co2_pe_error.py) the disproof of the prior "CO2/PE +5% is a factor/scope bug" lead: factors are spec-exact, scope identical, and the bias is per-cert demand fidelity (corr(SAP-err, PE-diff) = -0.54), not a one-slice factor fix. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| scripts | ||
| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
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| __init__.py | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
| Dockerfile.test.dockerignore | ||
| Makefile | ||
| MEMORY.md | ||
| next_claude_prompt.txt | ||
| 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