AN EFFICIENT HYBRID METHOD TO PREDICT WIND SPEED BASED ON LINEAR REGRESSION AND VMD

Yimei Yang, Jinping Liu, Yujun Yang, Jianhua Xiao and Abdulhameed F. Alkhateeb
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Yimei Yang: College of Computer and Artificial Intelligence, Huaihua University, 418008 Huaihua, P. R. China†College of Information Science and Engineering, Hunan Normal University, 410081 Changsha, P. R. China
Jinping Liu: ��College of Information Science and Engineering, Hunan Normal University, 410081 Changsha, P. R. China
Yujun Yang: College of Computer and Artificial Intelligence, Huaihua University, 418008 Huaihua, P. R. China
Jianhua Xiao: College of Computer and Artificial Intelligence, Huaihua University, 418008 Huaihua, P. R. China
Abdulhameed F. Alkhateeb: ��Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589 Jeddah, Saudi Arabia

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-16

Abstract: To effectively improve the power dispatching, the prediction accuracy of wind power has been the concern of many scholars for many years. The wind power prediction problem is actually equivalent to the wind speed prediction problem. Based on linear regression (LR) and variational mode decomposition (VMD), in this paper, we proposed an efficient hybrid method to predict wind speed. In the proposed method, the VMD is used to decompose the signal of wind speed into several sub-signal. Compared with the original wind-speed series, each sub-signal is a more stable subsequence signal. Then, we used the LR method to predict each subsequence signal. Eventually, we obtain the final prediction results of the original wind speed series merged the forecasting values of all subsequences signal. We selected two data to test our proposed method in our experiment. Compared with several comparison methods, we found that our proposed methods has better prediction performance than other methods from the experimental results.

Keywords: VMD; LR; Time Series Prediction; Wind Speed Forecasting; Hybrid Method (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401357

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