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Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

Author

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  • Guoqing An

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    Hebei Engineering Laboratory of Wind Power/Photovoltaic Coupling Hydrogen Production and Comprehensive Utilization, Shijiazhuang 050018, China)

  • Ziyao Jiang

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Libo Chen

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Xin Cao

    (Hebei Construction & Investment Group New Energy Company Ltd., Shijiazhuang 050051, China)

  • Zheng Li

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    Hebei Engineering Laboratory of Wind Power/Photovoltaic Coupling Hydrogen Production and Comprehensive Utilization, Shijiazhuang 050018, China)

  • Yuyang Zhao

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    Hebei Engineering Laboratory of Wind Power/Photovoltaic Coupling Hydrogen Production and Comprehensive Utilization, Shijiazhuang 050018, China)

  • Hexu Sun

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    Hebei Engineering Laboratory of Wind Power/Photovoltaic Coupling Hydrogen Production and Comprehensive Utilization, Shijiazhuang 050018, China)

Abstract

Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination ( R ²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.

Suggested Citation

  • Guoqing An & Ziyao Jiang & Libo Chen & Xin Cao & Zheng Li & Yuyang Zhao & Hexu Sun, 2021. "Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10453-:d:639327
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    References listed on IDEAS

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    1. Oh, Eunsung & Son, Sung-Yong, 2020. "Theoretical energy storage system sizing method and performance analysis for wind power forecast uncertainty management," Renewable Energy, Elsevier, vol. 155(C), pages 1060-1069.
    2. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
    3. Qunli Wu & Huaxing Lin, 2019. "Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    4. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    5. Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
    6. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-19, March.
    7. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    8. Yu Zhang & Jiawen Zhang & Lin Luo & Xiaorong Gao, 2019. "Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
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    Cited by:

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    5. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
    6. Yi Liang & Yingying Fan & Yongfang Peng & Haigang An, 2022. "Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    7. Antonio Lorenzo-Espejo & Alejandro Escudero-Santana & María-Luisa Muñoz-Díaz & Alicia Robles-Velasco, 2022. "Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study," Sustainability, MDPI, vol. 14(13), pages 1-25, June.

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