f0ab7446b2
Builds on S0380.66 (Appendix H pure helpers) + S0380.67 (W·h → kWh
unit fix) to assemble the spec-ordered H7 → H9 → … → H24 cascade
into a single entry point. Cert 000565's complete Appendix H input
set now flows through one call:
h24 = solar_water_heating_input_monthly_kwh(
collector_orientation=Orientation.W, collector_pitch_deg=30.0,
region=0, # UK average per rating
aperture_area_m2=3.0, # (H1)
zero_loss_efficiency=0.8, # (H2)
linear_heat_loss_a1=4.0, # (H3)
second_order_heat_loss_a2=0.01, # (H4)
loop_efficiency=0.9, # (H5)
incidence_angle_modifier=0.94, # (H6)
overshading_factor=0.8, # (H8) Table H2 "Modest"
overall_heat_loss_coefficient_from_test=6.5, # (H10) override
dedicated_solar_storage_volume_l=53.0, # (H12)
combined_cylinder_total_volume_l=160.0, # (H13)
hot_water_demand_monthly_kwh=..., # (62)m
wwhrs_monthly_kwh=(0,)*12, # (63a)m
cold_water_temperatures_monthly_c=TABLE_J1_TCOLD_FROM_MAINS_C,
external_temperatures_monthly_c=..., # (96)m
solar_hot_water_only=True,
)
New module surface:
- `monthly_collector_solar_flux_w_per_m2` — thin 12-month wrapper over
the existing `surface_solar_flux_w_per_m2` (Appendix U §U3.2
orientation + tilt polynomial). Cert 000565 collector: W, 30° pitch,
Thames Valley.
- `solar_water_heating_input_monthly_kwh` — chains all line-ref
helpers in spec order; returns (H24)m as a 12-tuple.
Tests:
- `test_monthly_collector_solar_flux_h7_returns_twelve_values_
matching_appendix_u` — smoke test pinning Jan < May < Jun shape
for the W-facing 30°-pitched collector.
- `test_solar_water_heating_input_monthly_kwh_returns_winter_zero_
summer_peak_shape` — orchestrator shape pin: 12-month tuple, all
non-negative, winter clamp to zero (Jan/Feb/Nov/Dec via Equation
H1's negative-X dominance), monotone Mar < May, Sep < Jun.
Magnitude pin against worksheet line 415 (Σ(H24)1..12 = 281.3478)
is DEFERRED to the next slice: current orchestrator output is
~510 kWh annual (1.8× the worksheet), traced to a spec ambiguity
between the top-level Equation H1 Y formula
(Y = Px · Aap · IAM · η0 · ηloop · Im · Hm / (1000 · Dm) — excludes
overshading H8) and the line-ref (H23) formulation
(Y = [(H18) · (H6) · (H5) · (H9) · ((41) · 24)] / [1000 · (H17)],
where (H9) = (H1) · (H2) · (H7) · (H8) includes H8). Both are
present in SAP 10.2 spec page 75-76 and differ by a factor of H8
(0.8 for cert 000565). Picking the spec-correct branch requires
either a worksheet trace of one cert's (H22)/(H23) intermediates or
a confirmed errata; the next slice runs that down and pins the
magnitude.
Test suite: 279 pass + 9 expected 000565 cascade-gap fails (unchanged
— orchestrator is not yet wired into `water_heating_from_cert`).
Pyright net-zero on both touched files.
Spec source: SAP 10.2 specification (14-03-2025) Appendix H pp.74-78
+ Appendix U §U3.2 page 127.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
||
|---|---|---|
| .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