A combined wind speed prediction model based on data processing, multi-objective optimization and machine learning

Abstract

The ability to estimate wind speed accurately is crucial for optimizing the utilization of wind energy in power systems. The randomness of wind speed and the performance of prediction models are the two issues that have the greatest impact on wind speed prediction. This paper starts from the two aspects of digging wind speed characteristics and optimizing model performance. A new wind speed combined prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved artificial bee colony algorithm (IABC) to optimize least squares support vector machine (LSSVM) performance. In order to reduce the randomness of the wind speed sequence and increase its predictability, CEEMDAN is first used to break down the wind speed sequence. Secondly, an LSSVM prediction model optimized by an improved artificial bee colony algorithm is established for each subsequence obtained by decomposition; finally, the wind speed forecast value is created by superimposing the results of each subsequence’s prediction. Case studies demonstrate that the combined prediction model put out in this study outperforms other models in terms of prediction performance.