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Next-Day Power Market Clearing Price Forecasting Using Artificial Fish-Swarm Based Neural Network

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

Market clearing price (MCP) is one of the most important factors impacting on power system. Taking into account the features of deregulation and fluctuation, this paper uses artificial neural network to forecast next-day MCP, with period-decoupled data sequence and wavelet transform. For the purpose of better performance, an improved learning algorithm of artificial fish-swarm is proposed. By simulating fish-swarm actions, in random searching for foods, artificial fish-swarm based neural network (AFNN) achieves global optimum. Comparing with traditional next-day MCP forecasting methods, the suggested method could achieve better adaptability and greater predictive accuracy, which was proved by the experimental results.

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References

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Author information

Authors and Affiliations

  1. Faculty of Software Engineering, Chongqing University, Chongqing, 400044, China

    Chuan Li & Shilong Wang

Authors
  1. Chuan Li
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  2. Shilong Wang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, C., Wang, S. (2006). Next-Day Power Market Clearing Price Forecasting Using Artificial Fish-Swarm Based Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_187

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  • DOI: https://doi.org/10.1007/11760023_187

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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