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Advances in Mathematical Physics
Volume 2014, Article ID 637017, 11 pages
Research Article

Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

1Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
2Mathematics Department, Science Faculty and Institute for Mathematical Research, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia

Received 24 April 2014; Revised 2 June 2014; Accepted 16 June 2014; Published 16 July 2014

Academic Editor: Hossein Jafari

Copyright © 2014 E. Faghihnia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.