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Boosting Wavelet Neural Networks Using Evolutionary Algorithms for Short-Term Wind Speed Time Series Forecasting

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11506))

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Abstract

This paper addresses nonlinear time series modelling and prediction problem using a type of wavelet neural networks. The basic building block of the neural network models is a ridge type function. The training of such a network is a nonlinear optimization problem. Evolutionary algorithms (EAs), including genetic algorithm (GA) and particle swarm optimization (PSO), together with a new gradient-free algorithm (called coordinate dictionary search optimization – CDSO), are used to train network models. An example for real speed wind data modelling and prediction is provided to show the performance of the proposed networks trained by these three optimization algorithms.

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Acknowledgments

This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/I011056/1, the Platform Grant EP/H00453X/1, and the EU Horizon 2020 Research and Innovation Programme Action Framework under grant agreement 637302.

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Authors and Affiliations

  1. Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK

    Hua-Liang Wei

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  1. Hua-Liang Wei
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Correspondence to Hua-Liang Wei .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Wei, HL. (2019). Boosting Wavelet Neural Networks Using Evolutionary Algorithms for Short-Term Wind Speed Time Series Forecasting. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20520-1

  • Online ISBN: 978-3-030-20521-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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