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Le Hoang Anh(레황안),Soo-Chang Lee(이수창),Gwang-Hyun Yu(유광현),Jin-Young Kim(김진영) 한국디지털콘텐츠학회 2024 한국디지털콘텐츠학회논문지 Vol.25 No.1
ChronoPatternNet revolutionizes power forecasting using a unique 2D convolutional approach for advanced temporal pattern recognition. The chronocycle hyperparameter, optimized via fast Fourier transform, structures Cyclical Time Frames, enhancing both extraction and prediction accuracy. Integration of layer normalization and residual learning mitigates the vanishing gradient problem, ensuring stability. With superior efficiency, ChronoPatternNet achieves a reduction in the number of parameters ranging from 58.8% to 61.9% compared to existing models. This positions ChronoPatternNet as a significant advancement in real-time energy management.