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.
Sch. of Hydropower & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Yonggang Wu ; Kit Po Wong ; Zhao Xu ; Zhao Yang Dong ; Herbert Ho-Ching Iu
Complexity theory, Entropy, Noise, Reliability, Time series analysis, Wind farms, Wind power generation