Multistep Wind Speed Prediction Based on the Fractional‐Order Longicorn Swarm Algorithm and the Two‐Stage Modal Decomposition Strategy.

Least squares support vector machine (LSSVM) and variational mode decomposition are common research methods for wind speed time series prediction. Addressing the challenge of selecting relevant parameters of LSSVM and VMD, a fractional‐order Beetle swarm optimization algorithm is proposed to optimize the relevant parameters. To weaken the negative impact of wind speed volatility on the accuracy of the prediction model, a two‐stage modal decomposition strategy based on extreme‐point symmetric mode decomposition, FO‐BSO algorithm, and VMD is proposed. ESMD is used to decompose the original wind speed time series. The diversity entropy is introduced as the criterion, and the FO‐BSO‐VMD secondary decomposition is applied for the high‐entropy modal components to further weaken the volatility of wind speed. Simulation results show that compared with the original LSSVM model, the ESMD‐FO‐BSO‐VMD‐LSSVM model presented in this study exhibits an average 44.10% increase in the fitting degree of wind speed prediction over three steps, which verifies the superior performance of the model in short‐term multistep wind speed prediction. [ABSTRACT FROM AUTHOR]