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Contains projects associated to the development of modeling products
981aaadf73
SAP 10.2 §12.4.4 (PDF p.36-37):
"Independent boilers that provide domestic hot water usually do so
throughout the year. With open fire back boilers or closed room
heaters with boilers, an alternative system (electric immersion)
may be provided for heating water in summer. In that case water
heating is provided by the boiler for months October to May and by
the alternative system for months June to September."
Scope is verbatim Table 4a codes 156 (Open fire with back boiler to
radiators) and 158 (Closed room heater with boiler to radiators). Range
cooker boilers (160, 161), pellet stoves with boilers (159), and
independent solid-fuel boilers (151, 153, 155) are NOT covered.
Pre-slice, the cascade treated the back-boiler cohort identically to
year-round solid-fuel mains: (59)m primary loss applied Jun-Sep, HW
fuel kWh was billed entirely at the boiler's solid-fuel rate, the HW
CO2 / PE factors used the boiler fuel's annual factor, and the off-peak
electric standing charge (£40 for 18-hour tariff) was not added because
the cert's lodged water-heating fuel code was anthracite.
Implementation (4 wired pieces):
1. `_section_12_4_4_summer_immersion_applies(epc, main)` — predicate
gate keyed on back-boiler SAP code (156, 158) + WHC ∈ {901, 902, 914}
"HW from main heating" + cylinder present.
2. `_primary_loss_override` zeroes (59)m for Jun-Sep when the predicate
fires — matches the Elmhurst P960 worksheet which has (59) Jun-Sep =
0 for SF2 (vs ~42 kWh/month for SF3 range cooker).
3. `_section_12_4_4_hw_blend(...)` — returns the 5-tuple
(annual_hw_fuel_kwh, blended_cost_gbp_per_kwh, blended_co2_factor,
blended_pe_factor, extra_standing_charge_gbp). The blend is kWh-
weighted across:
- Winter Oct-May: boiler fuel at the boiler's Table 32 unit price /
Table 12 annual CO2 / Table 12 annual PE factor
- Summer Jun-Sep: standard electricity (Table 12d/12e monthly
factors weighted by summer (62)m demand) priced at the tariff's
off-peak low rate per Table 13 note 2 (the 6.8 - 0.036V × N -
0.105V dual-immersion formula clamps to zero high-rate for
normal V/N combos on tariffs with ≥18 hrs low rate; SF2 has
V=110, N≈2 → 100% low-rate)
- The Table 32 off-peak electric standing charge that fires when
hot water uses off-peak electricity per Table 12 note (a). For
EIGHTEEN_HOUR tariff this is Table 32 code 38 = £40.
4. Orchestrator (`cert_to_inputs`) resolves the blend once and overrides
`hot_water_kwh_per_yr`, `hot_water_fuel_cost_gbp_per_kwh`,
`hot_water_co2_factor_kg_per_kwh`, `hot_water_primary_factor`, and
`standing_charges_gbp` when the predicate fires. Other certs fall
back to the existing single-fuel HW helpers (no behaviour change).
Worksheet evidence (heating-systems corpus property 001431 SF2 — code
158 + WHC=901 + cylinder thermostat + 18-hour tariff):
- (62) Oct-May = 2205.80 kWh, Jun-Sep = 684.55 kWh
- (217)m = 65 winter / 100 summer, (219) = 3393.5 anthr + 684.55 elec
= 4078.06 fuel kWh
- (247) HW cost = 4078.06 × 4.27 p/kWh blended = £174.25
- (251) Standing = £40 (off-peak electric standing only — solid fuel
has no standing charge)
- (255) Total = £801.13
Closures (SF2):
ΔSAP_c +1.86 → -0.0000 (EXACT)
Δcost -£42.84 → -£0.00 (EXACT)
ΔCO2 +346.87 → -93.10 kg/yr (residual: Elmhurst CO2 blend uses a
different summer-month weighting that
the SAP 10.2 Table 12d cascade does
not reproduce — spec-correct per
Table 12d header).
ΔPE -605.76 → -1027.51 kWh/yr (same spec-vs-Elmhurst PE blend
artifact via Table 12e monthly
cascade).
No regressions: 40/41 corpus variants unchanged (gate is narrow by SAP
code 156/158). Extended handover suite 898 pass / 0 fail. Pyright net-
zero (43 → 43).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| .devcontainer | ||
| .github/workflows | ||
| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| 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 | ||
| 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