Study of Wind Power Prediction in ELM Based on Improved SSA

This paper proposes a short‐term wind power prediction model based on the improved Sparrow Search Algorithm (SSA) and Extreme Learning Machine(ELM) for anomalous wind power information from wind farms. The objective is to enhance the accuracy of short‐term wind power prediction. The model employs the extraction of features utilizing raw wind power history data from wind farms, in conjunction with the application of Variable Importance in Projection indices in Partial Least Squares (PLS‐VIP). As the ELM network model is susceptible to the influence of randomly generated input weights and thresholds at the outset of training, a solution is proposed whereby the input weights and thresholds of the ELM are optimized using SSA. The optimal weights and thresholds identified by SSA are then applied to the ELM model, thus forming the SSA‐ELM model. To address the limitations of traditional SSA, namely its susceptibility to local optimal solutions and poor global search ability, an improved SSA‐ELM algorithm is proposed. The improved SSA‐ELM algorithm introduces chaotic sequences and an exchange learning strategy to the original SSA. The rationale behind incorporating chaotic sequences is to enhance the quality of the initial solution, ensuring a more uniform distribution of sparrow positions and, consequently, a more diverse sparrow population. This, in turn, enables the algorithm to achieve a more effective global search capability through the utilization of the exchange learning strategy. Subsequently, all the data are fed into the SSA‐ELM model for prediction purposes. The simulation results demonstrate that the model exhibits enhanced prediction accuracy and improved practical applicability in wind power prediction. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.