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Energies 2017, 10(4), 419; doi:10.3390/en10040419

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

School of Electrical Engineering, Wuhan University, Wuhan 430072, China
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Academic Editor: Lieven Vandevelde
Received: 10 January 2017 / Revised: 14 March 2017 / Accepted: 20 March 2017 / Published: 23 March 2017
(This article belongs to the Special Issue Sustainable Energy Technologies)
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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. View Full-Text
Keywords: wind power prediction; prediction intervals; variational mode decomposition; kernel extreme learning machine; artificial bee colony algorithm wind power prediction; prediction intervals; variational mode decomposition; kernel extreme learning machine; artificial bee colony algorithm
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Hu, M.; Hu, Z.; Yue, J.; Zhang, M.; Hu, M. A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction. Energies 2017, 10, 419.

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