Hybrid deep learning model for PV forecasting in scenarios with considerable fluctuations

Researchers in China conceived a new PV forecasting approach that integrates causal convolution, recurrent structures, attention mechanisms, and the Kolmogorov-Arnold Network (KAN). Experimental results demonstrate that the proposed model outperforms 10 existing forecasting models. Researchers from East China Normal University have developed a novel PV power prediction model for scenarios with considerable fluctuations. Dubbed CRAK, the new hybrid deep learning model combines a convolution layer, followed by a recurrent layer, an attention mechanism, and a Kolmogorov-Arnold Network (KAN). "The ...

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