Omar/data#4
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- add Ontario provincial EWRB download (15.6k rows, 2018-2024) - retrain electricity + gas models on combined 17k-row dataset (60 building types including Multifamily Housing, Retail, Office) - switch training target to intensity (kWh/m²); scale by GFA at inference - save/load LabelEncoder so inference encoding matches training exactly - replace XGBoost traffic model with ITE formula + TTC transit discount - add backend/calculators/traffic.py with per-type ITE rates - add gas intensity prediction; fix score direction label Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Kept omar/data version of energy_model.json, energy_model_meta.json, and train_models.py — these use the Ontario EWRB dataset (17k rows, 60 building types) which supersedes the intermediate format-detection patch that was on main. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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