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Contains projects associated to the development of modeling products
2adff08210
Per SAP 10.2 §4 line (64)m: `(64)m = max(0, (62)m + (63a)m + (63b)m
+ (63c)m + (63d)m)` where (63c)m is the solar HW credit lodged as a
negative quantity. The cascade hardcoded (63c)m = 0 since S0380.66
when the Appendix H orchestrator landed without integration, pending
the 1.81× over-count resolution (closed in S0380.74).
This slice plumbs the orchestrator into `water_heating_from_cert`
via a new `solar_water_heating_monthly_kwh_override` parameter, and
adds `_solar_hw_monthly_override` in cert_to_inputs.py that drives
the orchestrator from RdSAP 10 §10.11 Table 29 defaults +
cert-lodged collector geometry on Elmhurst Summary §16.0.
RdSAP 10 §10.11 Table 29 row "Solar panel" (p.58, verbatim):
"If solar panel present, the parameters for the calculation not
provided in the RdSAP data set are:
- panel aperture area 3 m²
- flat panel, η₀ = 0.80, a₁ = 4.0, a₂ = 0.01
- facing South, pitch 30°, modest overshading
- …
- pump for solar-heated water is electric (75 kWh/year)
- showers are both electric and non-electric"
Lodged collector orientation / pitch / overshading on the Summary
§16.0 ("Are details known? Yes" branch) override South / 30° /
Modest. Aperture, η₀, a₁, a₂, IAM stay at Table 29 defaults — the
deeper thermal parameter lodgement (P960 worksheet) isn't yet in
the Summary extractor surface.
For (H17)m to include storage + primary + combi losses, the cascade
runs a `demand_pass` call without solar (gets (62)m) before sizing
the solar credit. The final call then uses all overrides.
Files:
- datatypes/epc/surveys/elmhurst_site_notes.py: Renewables gains
`solar_hw_collector_orientation` / `_pitch_deg` / `_overshading`
optional fields.
- datatypes/epc/domain/epc_property_data.py: same three fields
added at the end of the dataclass.
- datatypes/epc/domain/mapper.py: from_elmhurst_site_notes
propagates the three new fields.
- backend/documents_parser/elmhurst_extractor.py: §16.0 section
parsing reads "Collector orientation" / "Collector elevation" /
"Overshading" rows; `_parse_solar_pitch_deg` strips the degree
glyph.
- domain/sap10_calculator/worksheet/water_heating.py: new
`solar_water_heating_monthly_kwh_override` param on
`water_heating_from_cert`; threaded into `output_from_water_
heater_monthly_kwh(solar_monthly_kwh=...)`.
- domain/sap10_calculator/rdsap/cert_to_inputs.py: Table 29
constants + `_solar_hw_monthly_override` helper +
`_orientation_from_summary_string` mapper. Added the demand_pass
intermediate call so (H17)m sees the full (62)m. Negates the
orchestrator output at the boundary (spec convention: heat
displaced from boiler is negative on line (63c)m).
Cert 000565 cascade pin shifts:
- hot_water_kwh_per_yr: +271.84 → −68.96 (4× closer)
- sap_score_continuous: +0.6334 → +0.7732 (drift downstream of HW)
- ecf: −0.0643 → −0.0784 (drift)
- total_fuel_cost: −56.08 → −68.36 (drift)
- co2: −19.77 → −22.66 (drift)
- sap_score (int): 29 EXACT (unchanged)
- space_heating / main_heating_fuel / lighting / pumps_fans:
unchanged
The remaining −69 kWh HW residual is the gap between Table 29
defaults (H12 = 75 L separate tank) and cert 000565's lodged H12 =
53 L + combined cylinder 160 L. Closing this requires extracting
solar storage volume + combined-cylinder routing from the cert (P960
worksheet block lodges these explicitly; Summary doesn't). That's
the follow-on slice.
Test baseline: 547 pass + 9 expected `test_sap_result_pin[000565-*]`
fails preserved. Cohort-2 + ASHP cohort + all golden fixtures
untouched (no certs other than 000565 lodge `solar_water_heating =
True`).
Pyright net-zero on touched files (68 errors at baseline = 68 errors
post-change).
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