Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22038
Title: An Advanced Approach for Construction of Optimal Wind Power Prediction Intervals
Authors: Zhang, G
Wu, Y
Wong, KP
Xu, Z 
Dong, ZY
Iu, HHC
Keywords: Ensemble empirical mode decomposition
Extreme learning machine
Prediction intervals
Sample entropy
Wind power
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2015, v. 30, no. 5, p. 2706-2715 How to cite?
Journal: IEEE transactions on power systems 
Abstract: High-quality wind power prediction intervals (PIs) are of utmost importance for system planning and operation. To improve the reliability and sharpness of PIs, this paper proposes a new approach in which the original wind power series is first decomposed and grouped into components of reduced order of complexity using ensemble empirical mode decomposition and sample entropy techniques. The methods for the prediction of these components with extreme learning machine technique and the formation of the overall optimal PIs are then described. The effectiveness of proposed approach is demonstrated by applying it to real wind farms from Australia and National Renewable Energy Laboratory. Compared to the existing methods without wind power series decomposition, the proposed approach is found to be more effective for wind power interval forecasts with higher reliability and sharpness.
URI: http://hdl.handle.net/10397/22038
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2014.2363873
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