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Contains projects associated to the development of modeling products
529100187e
Per SAP 10.2 Appendix N, PDF p.101 footnote 43 (line 7053):
"For the efficiency values, the interpolated efficiency is the
reciprocal of linear interpolation between the reciprocals of the
efficiencies."
i.e. 1/η_interp = (1 − t)·1/η_low + t·1/η_high, the weighted harmonic
mean at t = (PSR − PSR_low) / (PSR_high − PSR_low). Cascade was using
**linear** interpolation directly on η — a +0.15..+0.25% over-estimate
in the typical PSR range (1.2..1.5) for ASHPs in the cohort.
Cohort fixture: cert 3336-2825-9400-0512-8292 (Mitsubishi PUZ-WM50VHA,
PCDB 104568). MIT/η-zone cascade matches worksheet EXACTLY (every line
86..92, every month), but η_main_heating cascade 225.443 vs worksheet
224.923 → main_heating_fuel +5.24 kWh/yr too high → ECF 1.5474 vs ws
1.5503 → SAP +0.04 vs worksheet 78.3739. Back-solving the worksheet's
η_main implies η_space_1 = 224.923 / 0.95 ≈ 236.76.
Closed form at PSR=1.40151, bracketing PCDB rows PSR 1.2
(η_space_1=253.9) and PSR 1.5 (η_space_1=229.2):
Linear (pre-slice): 253.9 + (229.2 − 253.9) × 0.6717 = 237.31 ✗
Reciprocal (footnote 43): 1 / ((1 − 0.6717)/253.9 + 0.6717/229.2)
= 1 / 0.004224 = 236.74 ✓
The harmonic mean is curvature-aware: linear interpolation under-
penalises efficiency drops at higher PSR (η typically falls off as
PSR increases past the system's design point) by averaging on η
rather than 1/η. SAP 10.2 footnote 43 is explicit about which side
of the reciprocal the interpolation sits.
Outcome:
Cohort-2 Summary path (38 certs):
exact (<1e-4): 23 → **33** (+10)
≤±0.07: 15 → **5** (-10: HP certs close to exact)
±0.07..0.5: 0 → 0
±0.5..1: 0 → 0
±1+: 0 → 0
RAISES: 0 → 0
Cohort-2 HP cluster post-slice:
0100 +0.00003 ← was +0.00283
0320 -0.00001 ← was +0.01801
0330 -0.00004 ← was +0.01772
2336 +0.00003 ← was +0.01778
3336 +0.00001 ← was +0.04005 (worst residual closes exact)
4536 -0.00002 ← was +0.01312
9036 -0.00003 ← was +0.02159
9796 +0.00000 ← was +0.00174 (post-S0380.27)
2536 +0.00072 ← was +0.00163
2800 +0.00068 ← was +0.00436
4800 +0.00068 ← was +0.02939
9370 +0.00002 ← was +0.00174
9421 +0.00001 ← was +0.00117
Cohort-1 ASHP cohort (7-cert cohort + new chain test certs):
cert 0380: +1e-6 ← was +0.034 (Mitsubishi PUZ-WM50VHA, the
canonical first-HP cohort cert)
cert 3800: -2e-5 ← was +0.021
cert 9418: -3e-7 ← was +0.00004
cert 9285: -3e-5 ← was +0.021
cert 2636: -0.015 ← was +0.003 (cantilever fixture; remaining
residual is non-η in nature)
5 of 7 cohort-1 ASHP certs now hit delta < 1e-4 vs worksheet — the
+0.04 spec-precision-floor cluster diagnosed in
HANDOVER_CERT_0380_MIT_CASCADE.md is the linear-vs-reciprocal η
interpolation bug, not a spec-floor at all. The handover doc's "no
public spec or BRE data field would distinguish these" claim was
incorrect — SAP 10.2 footnote 43 is the resolution.
API path (golden fixtures): 6 ASHP cohort residuals updated to reflect
the cascade closure:
cert 0380 PE: -14.7865 → -14.6848 kWh/m²; CO2: +0.2774 → +0.2780 t/yr
cert 0350 PE: -7.9281 → -7.8741; CO2: +0.1697 → +0.1701
cert 2225 PE: -11.9175 → -11.8557; CO2: +0.2617 → +0.2621
cert 2636 PE: -9.7153 → -9.6692; CO2: +0.2189 → +0.2193
cert 3800 PE: -9.7551 → -9.6838; CO2: +0.2598 → +0.2603
cert 9285 PE: -8.1110 → -8.0466; CO2: +0.1559 → +0.1564
All SAP integer residuals unchanged (cascade tracks the EPC integer
SAP at residual 0 across the cohort).
PSR interpolation unit test (`test_interpolate_heat_pump_efficiency_at
_cert_0380_psr_per_sap_app_n`) updated to reflect the reciprocal
formula with the SAP-10.2-footnote-43 spec citation and closed-form
asserts (η_space_1 ≈ 234.5235; η_water_3 ≈ 285.0861 at PSR=1.43).
Pyright net-zero (1 → 1 across touched files: pcdb/parser.py,
tests/test_pcdb_table_362_lookup.py, rdsap/tests/test_golden_fixtures.py).
Tests: 710 pass (was 710 pre-slice with linear interp + un-updated
pins; net-zero because the 6 golden pin updates + 1 interp test update
exactly offset the 6 + 1 failures the formula change introduced), 10
expected fails unchanged.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs/adr | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
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| 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