39ae2cf0c2
SAP 10.2 Table 12d/12e: electric water heating on a 7-/10-hour tariff bills CO2/PE at the high-rate code (32/34) and low-rate code (31/33), kWh-weighted by the Table 13 high-rate fraction. The cost path already applied this split; the CO2/PE factors did not — they used the flat annual Table 12 figure (0.136 CO2 / 1.501 PE) for ALL dual-rate electric HW. That flat-annual behaviour (slice S0380.163) was validated only against HW-from-main "low-rate cost" certs (100% low, no high-rate split). It is NOT how Elmhurst bills a whc-903 ELECTRIC IMMERSION: the hand-built case-50 worksheet (000565 + dual immersion, 7-hour) splits HW CO2/PE into "high rate cost" (CO2 0.1475 / PE 1.5514) + "low rate cost" (CO2 0.1238 / PE 1.4429) weighted by the Table 13 fraction 0.1009. So flat-0.136 for immersion HW was a spec gap on our side, not an Elmhurst divergence. Fix: `_electric_immersion_hw_high_rate_fraction` threads the Table 13 fraction (scoped to whc-903, 7-/10-hour, cylinder data present) into the HW CO2 + PE factor helpers, which then blend the Table 12d/12e high/low codes. The flat rule is unchanged for HW-from-main and 18-/24-hour (no Table 12d split), so the S0380.163 41-variant cases and the existing pin are untouched. Case 50: rating CO2 2413.48 -> 2397.1237 = Elmhurst EXACT; demand CO2 2007.1384 EXACT; demand PE +111 -> +32.5 residual (within corpus PE noise). Corpus unchanged 73.3% / MAE 0.774 / CO2 0.08 / PE 3.4 (62 whc-903 off-peak certs; aggregate gauges hold). SAP unaffected (cost-based). Pin: test_whc903_immersion_hw_co2_pe_factors_split_high_low_on_off_peak; doc updated in SAP_CALCULATOR.md §8.1. pyright strict gate not run locally (pyright not installed in this container). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
||
|---|---|---|
| .claude/skills | ||
| .devcontainer | ||
| .github/workflows | ||
| .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 | ||
| .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 | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| 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