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International Conference on High Performance Computing and Applications

HPCA 2015: High Performance Computing and Applications pp 31–37Cite as

A TS-PSO Based Artificial Neural Network for Short-Term Load Forecast

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9576))

Abstract

(Aim) A short-term load forecast is an arduous problem due to the nonlinear characteristics of the load series. (Method) The artificial neural network (ANN) was employed. To train the ANN, a novel hybridization of Tabu Search and Particle Swarm Optimization (TS-PSO) methods was introduced. TS-PSO is a novel and powerful global optimization method, which combined the merits of both TS and PSO, and removed the disadvantages of both. (Results) Experiments demonstrated that the proposed TS-PSO-ANN is superior to GA-ANN, PSO-ANN, and BFO-ANN with respect to a mean squared error (MSE). (Conclusion) The TS-PSO-ANN is effective in a short-term load forecast.

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Acknowledgements

This paper was supported by NSFC (61273243, 51407095, 61503188), Natural Science Foundation of Jiangsu Province (BK20150982, BK20150983), Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), Key Supporting Science and Technology Program (Industry) of Jiangsu Province (BE2012201, BE2013012-2, BE2014009-3), Program of Natural Science Research of Jiangsu Higher Education Institutions (13KJB460011, 14KJB520021), Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), Education Reform Project in NJNU (18122000090615)

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, 1 Wenyuan, Nanjing, 210023, Jiangsu, China

    Shuihua Wang, Genlin Ji, Jiquan Yang, Xingxing Zhou & Yudong Zhang

  2. Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, 210042, Jiangsu, China

    Shuihua Wang, Genlin Ji, Jiquan Yang & Yudong Zhang

Authors
  1. Shuihua Wang
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  2. Genlin Ji
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  3. Jiquan Yang
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  4. Xingxing Zhou
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  5. Yudong Zhang
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Corresponding author

Correspondence to Yudong Zhang .

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Editors and Affiliations

  1. School of Computer Engineering, Shanghai University, Shanghai, China

    Jiang Xie

  2. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxin Chen

  3. Mathematics Department, University of Wyoming, Laramie, Wyoming, USA

    Craig C. Douglas

  4. School of Computer Engineering, Shanghai University, Shanghai, China

    Wu Zhang

  5. Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Yan Chen

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© 2016 Springer International Publishing Switzerland

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Wang, S., Ji, G., Yang, J., Zhou, X., Zhang, Y. (2016). A TS-PSO Based Artificial Neural Network for Short-Term Load Forecast. In: Xie, J., Chen, Z., Douglas, C., Zhang, W., Chen, Y. (eds) High Performance Computing and Applications. HPCA 2015. Lecture Notes in Computer Science(), vol 9576. Springer, Cham. https://doi.org/10.1007/978-3-319-32557-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-32557-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32556-9

  • Online ISBN: 978-3-319-32557-6

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