Wind power prediction based on EEMD-Tent-SSA-LS-SVM

Abstract

To solve the wind power prediction problem, the Improved Sparrow Search Algorithm-Least Squares Support Vector Machine (ISSA-LS-SVM) prediction model based on chaotic sequences is proposed to improve the convergence accuracy and shorten the prediction time of the prediction model. Firstly, the problem in the historical data is decomposed using an ensemble empirical modal algorithm. Then, wind speed series prediction is performed using the LS-SVM model. Finally, the wind turbine output power prediction is performed. The results show that compared with LS-SVM, SSA-LS-SVM and Tent-SSA-LS-SVM models, the EEMD-ISSA-LS-SVM prediction model has improved the convergence precision of wind power output predictive model, which is significant for the subsequent realization of optimal power dispatch.

Keywords

• Empirical modal decomposition of sets
• Chaotic sequences
• Improved sparrow search algorithm
• Least squares support vector machine algorithm