Journal of Applied Mathematics

An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting

Wen-Yeau Chang

Full-text: Open access


An accurate forecasting method for power generation of the wind energy conversion system (WECS) is urgently needed under the relevant issues associated with the high penetration of wind power in the electricity system. This paper proposes a hybrid method that combines orthogonal least squares (OLS) algorithm and genetic algorithm (GA) to construct the radial basis function (RBF) neural network for short-term wind power forecasting. The RBF neural network is composed of three-layer structures, which contain the input, hidden, and output layers. The OLS algorithm is used to determine the optimal number of nodes in a hidden layer of RBF neural network. With an appropriate RBF neural network structure, the GA is then used to tune the parameters in the network, including the centers and widths of RBF and the connection weights in second stage. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a WECS installed in Taichung coast of Taiwan. Comparisons of forecasting performance are made to the persistence method and back propagation neural network. The good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

Article information

J. Appl. Math., Volume 2013 (2013), Article ID 971389, 9 pages.

First available in Project Euclid: 14 March 2014

Permanent link to this document

Digital Object Identifier

Zentralblatt MATH identifier


Chang, Wen-Yeau. An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting. J. Appl. Math. 2013 (2013), Article ID 971389, 9 pages. doi:10.1155/2013/971389.

Export citation


  • A. M. Foley, P. G. Leahy, A. Marvuglia, and E. J. McKeogh, “Current methods and advances in forecasting of wind power generation,” Renewable Energy, vol. 37, no. 1, pp. 1–8, 2012.
  • P. Chen, T. Pedersen, B. Bak-Jensen, and Z. Chen, “ARIMA-based time series model of stochastic wind power generation,” IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 667–676, 2010.
  • L. Chen and X. Lai, “Comparison between ARIMA and ANN models used in short-term wind speed forecasting,” in Proceedings of the Asia-Pacific Power and Energy Engineering Conference (APPEEC '11), pp. 1–4, Wuhan, China, March 2011.
  • G. Sideratos and N. D. Hatziargyriou, “An advanced statistical method for wind power forecasting,” IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 258–265, 2007.
  • M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 915–920, 2009.
  • S. S. Soman, H. Zareipour, O. Malik, and P. Mandal, “A review of wind power and wind speed forecasting methods with different time horizons,” in Proceedings of the North American Power Symposium (NAPS '10), pp. 1–8, Arlington, Va, USA, September 2010.
  • M. Lange and U. Focken, “New developments in wind energy forecasting,” in Proceedings of the 2008 IEEE Power and Energy Society General Meeting, pp. 1–8, Pittsburgh, Pa, USA, July 2008.
  • M. Bhaskar, A. Jain, and N. V. Srinath, “Wind speed forecasting: present status,” in Proceedings of the 2010 International Conference on Power System Technology, pp. 1–6, Hangzhou, China, October 2010.
  • X. Zhao, S. X. Wang, and T. Li, “Review of evaluation criteria and main methods of wind power forecasting,” Energy Procedia, vol. 12, pp. 761–769, 2011.
  • X. C. Wang, P. Guo, and X. B. Huang, “A review of wind power forecasting models,” Energy Procedia, vol. 12, pp. 770–778, 2011.
  • M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou, and I. M. Manousaridis, “Short-term forecasting of wind speed and related electrical power,” Solar Energy, vol. 63, no. 1, pp. 61–68, 1998.
  • Y. K. Wu and J. S. Hong, “A literature review of wind forecasting technology in the world,” in Proceedings of the IEEE Conference on Power Tech, pp. 504–509, Lausanne, Switzerland, July 2007.
  • D. M. Zhao, Y. C. Zhu, and X. Zhang, “Research on wind power forecasting in wind farms,” in Proceedings of the 2011 IEEE Power Engineering and Automation Conference, pp. 178–178, Wuhan, China, September 2011.
  • C. M. Huang and F. L. Wang, “An RBF network with OLS and EPSO algorithms for real-time power dispatch,” IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 96–104, 2007.
  • M. Gao, J. Tian, and J. Xu, “Building logistics cost forecast based on high speed and precise genetic algorithm neural network,” in Proceedings of the International Workshop on Intelligent Systems and Applications (ISA '09), pp. 1–4, Wuhan, China, May 2009.
  • W. Y. Chang, “Wind energy conversion system power forecasting using radial basis function neural network,” in Proceedings of the 2nd International Conference on Engineering and Technology Innovation, Paper No. S1003, KaoHsiung, Taiwan, November 2012.
  • Y. M. Zhang, Y. B. Zhang, and L. Z. Lin, “Application of genetic algorithm and RBF neural network in network flow prediction,” in Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT '10), pp. 298–301, Chengdu, China, July 2010.
  • Z. Yun, Z. Quan, S. Caixin, L. Shaolan, L. Yuming, and S. Yang, “RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 853–858, 2008.
  • S. Chen, C. F. N. Cowan, and P. M. Grant, “Orthogonal least squares learning algorithm for radial basis function networks,” IEEE Transactions on Neural Networks, vol. 2, no. 2, pp. 302–309, 1991.
  • W. D. Qian, “Road pavement performance evaluation model based on hybrid ybrid genetic algorithm neural network,” in Proceedings of the 2nd International Conference on Computational Intelligence and Natural Computing (CINC '10), pp. 209–212, Wuhan, China, September 2010.
  • Y. J. Li, X. Q. Sun, and L. M. Zhang, “Logistics forecasting technology by RBF neural network trained by genetic algorithm,” in Proceedings of the 2nd International Conference on Computer Engineering and Technology, vol. 7, pp. V7-513–V7-516, Chengdu, China, July 2010.