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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

    as
    1. Amr Khaled Khamees & Almoataz Y. Abdelaziz & Makram R. Eskaros & Adel El-Shahat & Mahmoud A. Attia, 2021. "Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method," Energies, MDPI, vol. 14(19), pages 1-19, September.
    2. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    3. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    4. Xiaoxiao Qian & Cheng Tang & Yuki Todo & Qiuzhen Lin & Junkai Ji, 2020. "Evolutionary Dendritic Neural Model for Classification Problems," Complexity, Hindawi, vol. 2020, pages 1-13, August.
    5. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    6. Jiang, Ping & Yang, Hufang & Heng, Jiani, 2019. "A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting," Applied Energy, Elsevier, vol. 235(C), pages 786-801.
    7. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
    8. Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 2017. "A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China," Renewable Energy, Elsevier, vol. 102(PA), pages 241-257.
    9. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    10. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
    11. Long, Wen & Jiao, Jianjun & Liang, Ximing & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2022. "Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm," Energy, Elsevier, vol. 249(C).
    12. Zhao, Xuejing & Wang, Chen & Su, Jinxia & Wang, Jianzhou, 2019. "Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system," Renewable Energy, Elsevier, vol. 134(C), pages 681-697.
    13. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    14. Helong Yu & Shimeng Qiao & Ali Asghar Heidari & Chunguang Bi & Huiling Chen, 2022. "Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design," Mathematics, MDPI, vol. 10(2), pages 1-35, January.
    15. Xiaohan Huang & Aihua Jiang, 2022. "Wind Power Generation Forecast Based on Multi-Step Informer Network," Energies, MDPI, vol. 15(18), pages 1-17, September.
    16. Shahram Hanifi & Saeid Lotfian & Hossein Zare-Behtash & Andrea Cammarano, 2022. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models," Energies, MDPI, vol. 15(19), pages 1-21, September.
    17. Qiuhong Huang & Xiao Wang, 2022. "A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion," Energies, MDPI, vol. 15(15), pages 1-19, July.
    18. Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
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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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