LoadKAN combines feature-isolated temporal attention with KAN to produce competitive load forecasts on three U.S. markets and enables quantitative analysis of non-linear mobility-load relationships via learned activation functions.
Electrical load forecasting models: A critical systematic review
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Contextually-enhanced transformers integrating timetable and occupancy data achieve 26.6% and 56.3% average MAE reductions in railway and building energy forecasting respectively, outperforming prior methods.
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Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
LoadKAN combines feature-isolated temporal attention with KAN to produce competitive load forecasts on three U.S. markets and enables quantitative analysis of non-linear mobility-load relationships via learned activation functions.
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Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Models
Contextually-enhanced transformers integrating timetable and occupancy data achieve 26.6% and 56.3% average MAE reductions in railway and building energy forecasting respectively, outperforming prior methods.