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The neighbour SAP-divergence cohort was (postcode, property_type, built_form), which mixed electric- and gas-heated dwellings. Electricity scores materially lower in SAP than mains gas for identical fabric, so a mixed-fuel postcode produced cross-fuel false outliers — an electric dwelling flagged only for being electric among gas neighbours. Add the main-heating fuel class to the cohort key (electricity variants — 29/30 + off-peak 31-40 — collapse to one class; every other code is its own bucket). Now every flagged divergence is a same-fuel comparison worth investigating. On portfolio 814 this refined 20 raw divergences to 17 same-fuel ones while keeping the genuine within-fuel outliers (e.g. the all-electric WC2B 4AW flats at SAP 26/38 vs a cohort median of 67). Reaches the fuel via epc_main_heating_detail through the indexed property_id — still never touches the recommendation table. Surfaced by the portfolio-814 recommendation audit (Work item B, #1388). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| .. | ||
| __init__.py | ||
| anomalies.py | ||
| neighbour_divergence.py | ||