Wind power prediction plays a significant role in enhancing the effectiveness of power system operation and decision-making. Given the inherent stochastic nature of meteorological events, achieving highly accurate forecasts for wind power poses considerable challenges. To address this challenge, this paper initially leverages the time series learning capability of recurrent neural networks (RNN) to extract sequential information from historical wind power data. Subsequently, the information extracted from the convolutional layer is transferred ...
