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Towards novel deep neuroevolution models: chaotic levy grasshopper optimization for short-term wind speed forecasting

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Abstract

High accurate wind speed forecasting plays an important role in ensuring the sustainability of wind power utilization. Although deep neural networks (DNNs) have been recently applied to wind time-series datasets, their maximum performance largely leans on their designed architecture. By the current state-of-the-art DNNs, their architectures are mainly configured in manual way, which is a time-consuming task. Thus, it is difficult and frustrating for regular users who do not have comprehensive experience in DNNs to design their optimal architectures to forecast problems of interest. This paper proposes a novel framework to optimize the hyperparameters and architecture of DNNs used for wind speed forecasting. Thus, we introduce a novel enhanced version of the grasshopper optimization algorithm called EGOA to optimize the deep long short-term memory (LSTM) neural network architecture, which optimally evolves four of its key hyperparameters. For designing the enhanced version of GOA, the chaotic theory and levy flight strategies are applied to make an efficient balance between the exploitation and exploration phases of the GOA. Moreover, the mutual information (MI) feature selection algorithm is utilized to select more correlated and effective historical wind speed time series features. The proposed model’s performance is comprehensively evaluated on two datasets gathered from the wind stations located in the United States (US) for two forecasting horizons of the next 30-min and 1-h ahead. The experimental results reveal that the proposed model achieves the best forecasting performance compared to seven prominent classical and state-of-the-art forecasting algorithms.

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Acknowledgements

This research was partially supported by the Australian Research Council Discovery Projects funding scheme (project DP190102181 and DP210101465).

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Authors and Affiliations

  1. Institute for Intelligent Systems Research and Innovation, (IISRI), Deakin University, Geelong, Australia

    Seyed Mohammad Jafar Jalali, Abbas Khosravi & Saeid Nahavandi

  2. Department of Information Technology, Kermanshah University of Technology, Kermanshah, Iran

    Sajad Ahmadian & Vahid Ghasemi

  3. Department of Computer Science, University of Tulsa, Tulsa, OK, USA

    Mahdi Khodayar

  4. School of Technology and Innovations, University of Vaasa, 65200, Vaasa, Finland

    Miadreza Shafie-khah

  5. Faculty of Engineering of University of Porto and INESC TEC, Porto, Portugal

    João P. S. Catalão

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  1. Seyed Mohammad Jafar Jalali
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Jalali, S.M.J., Ahmadian, S., Khodayar, M. et al. Towards novel deep neuroevolution models: chaotic levy grasshopper optimization for short-term wind speed forecasting. Engineering with Computers 38 (Suppl 3), 1787–1811 (2022). https://doi.org/10.1007/s00366-021-01356-0

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  • DOI: https://doi.org/10.1007/s00366-021-01356-0

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