MSGCN-xLSTM: Efficient Wind Power Forecasting Approach Combining Multiscale Graph Convolutional Network and Extended LSTM

Wind power generation is an important approach to achieving clean energy and is associated with notable randomness and uncertainty. Wind power data exhibit strong periodicity and historical dependence, which are influenced by wind speed, temperature, and other environmental factors. Existing methods struggle to simultaneously capture complex time-space dependence and dynamic changes in wind power data, resulting in limited forecasting accuracy and generalization ability. To address these challenges in wind power forecasting (WPF), a method that can comprehensively account for the characteristics of spatiotemporal interaction and the long- and short-term dependencies in the data is urgently needed. Therefore, an efficient WPF method combining a multiscale graph convolutional network (MSGCN) and an extended long short-term memory (xLSTM) is proposed, referred to as the MSGCN-xLSTM. First, an MSGCN is designed to extract the spatial characteristics of each node, obtaining feature information across multiple timescales through multiscale graph convolution (MSGC) operations, where the characteristics of nodes and their neighbors are weighted and fused. Second, the global feature enhancement mechanism is employed to adjust the characteristics extracted over different timescales, capturing long-distance global dependencies. Third, the improved xLSTM is applied to model wind power time series characterized by strong periodicity and multivariate dependence, enhancing the capturing of long- and short-term dependencies. Finally, an enhanced global exploration-whale optimization algorithm (EGE-WOA) is used to adaptively optimize the hyperparameters of the model, thereby improving its adaptability to complex wind power data. Furthermore, extensive experiments conducted on historical datasets from real wind farms demonstrate that the proposed method effectively captures the complex interactions and dynamic changes in spatiotemporal characteristics of wind power data. The proposed method not only surpasses comparison models in forecasting accuracy and generalization on datasets from different wind farms but also excels in training efficiency and convergence.