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Short-term wind power prediction based on improved small-world neural network

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Abstract

In a competitive electricity market, wind power prediction is important for market participants. However, the prediction has not a general solution due to its inherent uncertainty, intermittency, and multi-fractal nature. This paper firstly constructs a small-world BP neural network (SWBP) with weight convergence and statistics analysis in order to build a maximum approximation for its nonlinear computation. Then, a modified mutual information (MI) is presented to select the input features for the SWBP, whose selection criteria is to establish the relationship between the numerous candidate features of the input and output associated with the wind power prediction by eliminating the redundant. Thirdly, the improved SWBP based on the modified MI is compared with the BP network upon the 15-min-ahead wind power prediction for performance testing, which includes convergence, training time, and forecast accuracy. Moreover, mean value method is adopted to smooth the volatility of selected input. At last, illustrative examples based on the 4-h-ahead rolling prediction are given to demonstrate its stability, validity, and accuracy of the proposed methodology contrasted with the BP, PSOBP, and RBF neural network algorithms.

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Acknowledgements

This work is funded by National Natural Science Foundation of China (50776005) and supported by China Scholarship Council.

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Authors and Affiliations

  1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Shuang-Xin Wang, Meng Li & Long Zhao

  2. Department of Electrical Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Chen Jin

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  1. Shuang-Xin Wang
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  2. Meng Li
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  3. Long Zhao
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  4. Chen Jin
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Correspondence to Shuang-Xin Wang.

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Wang, SX., Li, M., Zhao, L. et al. Short-term wind power prediction based on improved small-world neural network. Neural Comput & Applic 31, 3173–3185 (2019). https://doi.org/10.1007/s00521-017-3262-7

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  • DOI: https://doi.org/10.1007/s00521-017-3262-7

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