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Codex/fase 7 - #13
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- train a RandomForestRegressor using the chronological dataset split - calculate MAE, RMSE, and R² for the model and baseline - persist the model and training metadata with Joblib - validate artifact format and feature compatibility on load - add explicit errors for missing or incompatible artifacts - add reproducible training command and document actual metrics - cover training, persistence, temporal split, and baseline comparison
- add admin endpoint for explicit demand prediction execution - build causal inference features from available station data - persist 60-minute forecasts with effective capacity and risk levels - generate deterministic recommendations and deduplicated PEAK_RISK alerts - keep ML advisory without changing energy allocations - add authorization, boundary, persistence, failure, and safety tests - update OpenAPI, Docker configuration, and Phase 7 documentation
…oard - display forecast demand, target time, model version, and recommendation - translate peak risk levels and add accessible visual indicators - distinguish expired, missing, insufficient-data, unavailable-model, and communication-error states - prevent forecasts from leaking across station filters - add responsive styling and comprehensive dashboard tests
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