afdf297f3b
Heat-network certs (cat=6) were under-predicted in cost — SAP bias
+6.31 across 13 sample certs, PE bias -15.6 (we under-predicted PE).
Root cause: missing distribution-loss-factor application.
SAP 10.2 spec references:
- Table 12 note (k): "Cost is per unit of heat generated (i.e.
before distribution losses); emission and primary factors are per
unit of fuel used by the heat generator."
- §C3.1: "Where a heat network is listed in the PCDB, the DLF is
already factored into the cost, CO2 and PE factors recorded
therein, so a DLF of 1 should be entered in worksheet (306) to
avoid double counting." (Implication: non-PCDB networks MUST
apply DLF.)
- Table 12c (p. 193): DLF by age band, 1.20 (A pre-1900) →
1.50 (K+ 2007+).
- RdSAP 10 §10.11 Table 29 cross-references Table 12c.
Mechanism: setting main_heating_efficiency = 1/DLF (and water_eff
when HW inherits from main via codes 901/902/914) makes the
calculator's main_fuel_kwh = q_useful × DLF = q_generated, which
multiplied by the per-kWh-generated unit price gives the cost the
spec mandates.
Affects:
- Heat-network main heating (sap_main_heating_code in 301-304 OR
main_heating_category == 6)
- HW from main on such certs (water_heating_code in 901/902/914)
Trade-off: CO2/PE for heat-network certs will under-predict ~20%
versus the spec's "fuel-burned × per-fuel-factor" formula, because
our architecture uses one main_fuel_kwh value for cost AND CO2/PE.
For SAP-rating purposes (the priority) this is acceptable; the PE
bias actually moves in the right direction here (cat=6 PE bias
-15.6 → -5.6) because the under-counting partially cancels a
pre-existing larger under-count.
Parity probe at 300 certs, seed=7:
SAP MAE 4.69 → 4.61 (-0.08)
SAP bias 0.98 → 0.87 (-0.11)
PE MAE 43.32 → 43.11 (-0.21)
cat=6 PE bias -15.6 → -5.6 (+10.0, correct direction)
cat=6 PE MAE 40.3 → 35.8 (-4.5)
cat=6 our_pe 158.5 → 225.0 (cert 230.6 — converged)
Cumulative across S-B23 → S-B31:
SAP MAE 5.34 → 4.61 (-0.73)
PE MAE 57.28 → 43.11 (-14.17)
PE bias 51.56 → 38.64 (-12.92)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| docs | ||
| epr_data_exports | ||
| etl | ||
| infrastructure/terraform | ||
| model_data/requirements | ||
| packages | ||
| recommendations | ||
| scripts | ||
| services | ||
| sfr/principal_pitch | ||
| survey_report | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| AGENTS.md | ||
| 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_backlog.sh | ||
| 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