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An Improved SA-Based BP-ANN Technique for Annual Runoff Forecasting Under Uncertain Environment

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Proceedings of the Seventh International Conference on Management Science and Engineering Management

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 242))

Abstract

In this paper, the author presents an integrated approach combining the simulated annealing method and the feed forward neural network to forecast the annual runoff in power system under uncertain environment. The type of neural network used in this method is a multi-layer pre-trained by the SA. Finally, we use the SA-based ANN to see if we actually could reduce the error of annual runoff forecasting. The proposed Simulated Algorithm-based Error Back Propagation Artificial Neural Net (SA-based BP-ANN) annual forecasting scheme was tested using data obtained from a case study including 24 h time periods. The result demonstrated the accuracy of the proposed annual runoff forecasting.

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Author information

Authors and Affiliations

  1. Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu, 610065, People’s Republic of China

    Qiurui Liu

Authors
  1. Qiurui Liu
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Corresponding author

Correspondence to Qiurui Liu .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, China, People's Republic

    Jiuping Xu

  2. Drexel University, Philadelphia, PA, USA

    John A. Fry

  3. LeBow College of Business Department of Decision Sciences, Drexel University, Philadelphia, Pennsylvania, USA

    Benjamin Lev

  4. Azerbaijan National Academy of Sciences, Institute of Cybernetics, Baku, Azerbaijan

    Asaf Hajiyev

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Liu, Q. (2014). An Improved SA-Based BP-ANN Technique for Annual Runoff Forecasting Under Uncertain Environment. In: Xu, J., Fry, J., Lev, B., Hajiyev, A. (eds) Proceedings of the Seventh International Conference on Management Science and Engineering Management. Lecture Notes in Electrical Engineering, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40081-0_125

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  • DOI: https://doi.org/10.1007/978-3-642-40081-0_125

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40080-3

  • Online ISBN: 978-3-642-40081-0

  • eBook Packages: Business and EconomicsBusiness and Management (R0)

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