7b30b464e5
MVHR (24a) heat-recovery support, part 2: the mapper + cascade wiring. Both source paths now resolve balanced whole-house MV with heat recovery to the MVHR kind: - gov-API: `_API_MECHANICAL_VENTILATION_TO_KIND` 4 → "MVHR" (was None / treated as natural — under-stated ventilation heat loss, over-rating). - Elmhurst Summary: `_ELMHURST_MV_TYPE_TO_KIND` "Mechanical ventilation with heat recovery (MVHR)" → "MVHR" (was UnmappedElmhurstLabel, which blocked the whole Summary for MVHR dwellings). cert_to_inputs resolves the in-use heat-recovery efficiency + SFP for an MVHR cert (`_mvhr_system_values`): pick the PCDB Table 323 data point by the lodged wet-room count (SAP 10.2 §2.6.4), multiply the raw efficiency by the Table 329 ducts-inside-envelope in-use factor (0.90) and the raw SFP by the per-duct-type factor (rigid 1.4), and feed: - the §2.6.6 eq (2) effective-air-change credit (23c) → (24a)/(25)m; - the (230a) fan electricity (in-use SFP × 1.22 × V), costed but NOT added to the Table 5a gains (its effect is in the efficiency). An MVHR lodged with no PCDF index falls back to the SAP 10.2 Table 4g default (raw efficiency 66% × 0.70, raw SFP 2.0 × 2.5). Worksheet-proven on simulated case 49 (000565 semi + Vent Axia Sentinel Kinetic B 500140 + gas combi → Elmhurst Current SAP 72): every MVHR line matches Elmhurst exactly — (33) fabric heat loss 100.5923, (23c) in-use efficiency 81.9% = 91 × 0.90, (25)m Jan 0.8571, (230a) fan electricity 415.9325, (231) total pumps/fans 501.9325. The residual SAP 71 vs 72 is the known 000565-family space-heating-demand artifact (same -1/-2 seen on cases 47/48), not the MVHR logic. Corpus: within-0.5 72.6% -> 72.7%, MAE 0.788 -> 0.782, PE 3.6 -> 3.5. The 3 gov-API MVHR certs: Flat 1 +6 -> 0 (Table 4g default path) and 12a Princes Gate +3 -> +1 (heat-recovery credit); Apartment 707 -4 -> -6 is a separate baseline under-rate (it under-rated as natural too — the MVHR credit correctly adds ventilation loss per Elmhurst's method). Ratcheted _MAX_SAP_MAE 0.79 -> 0.785, _MAX_PE_PER_M2_MAE 3.7 -> 3.6. Note: pyright strict type gate not run locally (pyright not installed). 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 | ||
| 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