Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16454
Title: Probabilistic forecasting of wind power generation using extreme learning machine
Authors: Wan, C
Xu, Z 
Pinson, P
Dong, ZY
Wong, KP
Keywords: Bootstrap
Extreme learning machine (ELM)
Forecasting
Prediction interval
Wind power
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2014, v. 29, no. 3, 6665108, p. 1033-1044 How to cite?
Journal: IEEE transactions on power systems 
Abstract: Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.
URI: http://hdl.handle.net/10397/16454
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2013.2287871
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