A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

Mengyue Hu, Zhijian Hu, Jingpeng Yue, Menglin Zhang and Meiyu Hu
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Mengyue Hu: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Zhijian Hu: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Jingpeng Yue: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Menglin Zhang: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Meiyu Hu: School of Electrical Engineering, Wuhan University, Wuhan 430072, China

Energies, 2017, vol. 10, issue 4, 1-15

Abstract: Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.

Keywords: wind power prediction; prediction intervals; variational mode decomposition; kernel extreme learning machine; artificial bee colony algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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