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Rational and Self-adaptive Evolutionary Extreme Learning Machine for Electricity Price Forecast

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Proceedings of ELM-2015 Volume 2

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 7))

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Abstract

Electricity price forecast is of great importance to electricity market participants. Given the sophisticated time-series of electricity price, various approaches of extreme learning machine (ELM) have been identified as effective prediction approaches. However, in high dimensional space, evolutionary extreme learning machine (E-ELM) is time-consuming and difficult to converge to optimal region when just relying on stochastic searching approaches. In the meanwhile, due to the complicated functional relationship, objective function of E-ELM seems difficult also to be mined directly for some useful mathematical information to guide the optimum exploring. This paper proposes a new differential evolution (DE) like algorithm to enhance E-ELM for more accurate and reliable prediction of electricity price. An approximation model for producing DE-like trail vector is the key mechanism, which can use simpler mathematical mapping to replace the original yet complicated functional relationship within a small region. Thus, the evolutionary procedure frequently dealt with some rational searching directions can make the E-ELM more robust and faster than supported only by the stochastic methods. Experimental results show that the new method can improve the performance of E-ELM more efficiently.

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Acknowledgment

This work is supported in part by the Australian Research Council (ARC) through a Linkage Project (grant no. 120100302), in part by the University of Newcastle through a Faculty Strategic Pilot Grant, in part by the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 14A136). The author would like to thank Prof. Qingfu Zhang (UK and Hongkong) for fruitful discussions and patient tutoring. Thank Dr. Jingqiao Zhang for providing the source code of JADE.

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Authors and Affiliations

  1. The Centre for Intelligent Electricity Networks, University of Newcastle, Newcastle, NSW, 2308, Australia

    Chixin Xiao, Yan Xu, Ke Meng & Xun Zhou

  2. The College of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Chixin Xiao

  3. The School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, 2006, Australia

    Zhaoyang Dong & Xin Zhang

Authors
  1. Chixin Xiao
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  2. Zhaoyang Dong
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  3. Yan Xu
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  4. Ke Meng
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  5. Xun Zhou
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  6. Xin Zhang
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Corresponding author

Correspondence to Chixin Xiao .

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Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

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Xiao, C., Dong, Z., Xu, Y., Meng, K., Zhou, X., Zhang, X. (2016). Rational and Self-adaptive Evolutionary Extreme Learning Machine for Electricity Price Forecast. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-28373-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28372-2

  • Online ISBN: 978-3-319-28373-9

  • eBook Packages: EngineeringEngineering (R0)

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