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Short-Term Wind Speed Forecast Based on B-Spline Neural Network Optimized by PSO

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  • Zhongqiang Wu
  • Wenjing Jia
  • Liru Zhao
  • Changhan Wu

Abstract

Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm optimization is proposed to predict the short-term wind speed. The B-spline neural network can change the division of input space and the definition of basis function flexibly. For any input, only a few outputs of hidden layers are nonzero, the outputs are simple, and the convergence speed is fast, but it is easy to fall into local minimum. The traditional method to divide the input space is thoughtless and it will influence the final prediction accuracy. Particle swarm optimization is adopted to solve the problem by optimizing the nodes. Simulated results show that it has higher prediction accuracy than traditional B-spline neural network and BP neural network.

Suggested Citation

  • Zhongqiang Wu & Wenjing Jia & Liru Zhao & Changhan Wu, 2015. "Short-Term Wind Speed Forecast Based on B-Spline Neural Network Optimized by PSO," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-7, April.
    • Handle: RePEc:hin:jnlmpe:278635
    DOI: 10.1155/2015/278635
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