Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31480
Title: Optimal prediction intervals of wind power generation
Authors: Wan, C
Xu, Z 
Pinson, P
Dong, ZY
Wong, KP
Keywords: Extreme learning machine
Forecasts
Particle swarm optimization
Prediction intervals
Wind power
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2014, v. 29, no. 3, 6662465, p. 1166-1174 How to cite?
Journal: IEEE transactions on power systems 
Abstract: Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems.
URI: http://hdl.handle.net/10397/31480
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2013.2288100
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