Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13767
Title: Direct interval forecasting of wind power
Authors: Wan, C
Xu, Z 
Pinson, P
Keywords: Extreme learning machine
Forecasting
Particle swarm optimization
Prediction interval
Wind power
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2013, v. 28, no. 4, p. 4877-4878 How to cite?
Journal: IEEE transactions on power systems 
Abstract: This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learningmachine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness, without the prior knowledge of forecasting errors. The proposed approach has been proved to be highly efficient and reliable through preliminary case studies using real-world wind farm data, indicating a high potential of practical application.
URI: http://hdl.handle.net/10397/13767
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2013.2258824
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