0012ad1625
A PasHub Manual-Entry electric boiler lodges "Direct acting" in its "Heating System (Boiler):" age-band cell. `_PASHUB_MANUAL_BOILER_AGE_BAND_TO_TABLE_4B` covers only gas/liquid Table 4b rows, so the descriptor resolved to None and the boiler stayed uncoded — taking the generic 0.80 gas-boiler seasonal efficiency AND, on a Dual meter, `_table_12a_system_for_main`'s None-fallback that bills the whole main-heating load at the off-peak LOW rate. The two errors pull opposite ways, so an identical mis-mapping over-rated one dwelling and under-rated another (fixture 497712825571 +9.5 on a Dual meter; 461386632387 -6.2 on a Single meter). A direct-acting electric "boiler with radiators" is SAP 10.2 Table 4a code 191 (direct-acting electric boiler, efficiency 1.00, §12 Rule 3 tariff). Code it directly in the manual-boiler resolver (gated on fuel Electricity + no PCDB product), so the single code value drives the efficiency table, the Table 12a pricing row and the tariff dispatch together. Both fixtures converge: 497712825571 → +0.6, 461386632387 → +0.4. LRHA within-0.5 52.4% → 53.4%, MAE 0.835. Renamed `_pashub_manual_boiler_table_4b_code` → `_pashub_manual_ boiler_code` (it now also returns the Table 4a electric code). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| .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 | ||
| modelling_audit.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