of operating conditions, spatial features that effectively characterize load power are extracted. Based on the composite spatial-temporal features and deep learning, online ultra-short-term load power prediction achieved. Experiments conducted on real-world subway lines, supported by comparative analyses of feature engineering and model engineering, demonstrate that the method significantly enhances prediction accuracy and generalization.">

Ultra-Short-Term Load Power Forecasting Method of Urban Rail TPSS Based on Local Information Spatial-Temporal Features (original) (raw)

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