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Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

Author

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  • Mohammed A. A. Al-qaness

    (College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China)

  • Ahmed A. Ewees

    (College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia
    Department of Computer, Damietta University, Damietta 34517, Egypt)

  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13518, Lebanon)

  • Ahmed H. Samak

    (College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia
    Faculty of Science, Menofia University, Shibeen El-Kom 32511, Egypt)

Abstract

It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R 2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets.

Suggested Citation

  • Mohammed A. A. Al-qaness & Ahmed A. Ewees & Mohamed Abd Elaziz & Ahmed H. Samak, 2022. "Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer," Energies, MDPI, vol. 15(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9261-:d:995615
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    References listed on IDEAS

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    Cited by:

    1. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    2. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).

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