Title: Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm

Authors: Dingcheng Wang; Yiyi Lu; Beijing Chen; Youzhi Zhao

Addresses: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract: As a kind of clean energy, wind energy is widely disseminated and has been widely researched. Compared with other methods, the support vector machine algorithm is more logical. Least squares support vector machine can improve training efficiency. Therefore, the method of multi-output least squares support vector regression is used to forecast the wind speed and wind direction in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR. Compared with single output support vector machines, multi-output support vector machine readily solves problems of complex structure. The simulation model is established to predict the value of wind speed and wind direction by using different algorithms. The simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.

Keywords: wind speed and wind direction forecasting; multi-output least square support vector regression; bat algorithm.

DOI: 10.1504/IJES.2020.105936

International Journal of Embedded Systems, 2020 Vol.12 No.2, pp.137 - 145

Received: 30 Aug 2017
Accepted: 24 May 2018
Published online: 19 Mar 2020 *

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