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A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model

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  • Tonglin Fu

    (School of Mathematics & Statistics, LongDong University, Qingyang 745000, China)

  • Chen Wang

    (School of Information Science and Engineering Lanzhou University, Lanzhou 730000, China)

Abstract

Wind power has the most potential for clean and renewable energy development. Wind power not only effectively solves the problem of energy shortages, but also reduces air pollution. In recent years, wind speed time series analyses have increasingly become a concern of administrators and power grid dispatchers searching for a reasonable way to reduce the operating cost of wind farms. However, analyzing wind speed in detail has become a difficult task, because the traditional models sometimes fail to capture data features due to the randomness and intermittency of wind speed. In order to analyze wind speed series in detail, in this paper, an effective and practical analysis system is studied and developed, which includes a data analysis module, a data preprocessing module, a parameter optimization module, and a wind speed forecasting module. Numerical results show that the wind time series analysis system can not only assess wind energy resources of a wind farm, but also master future changes of wind speed, and can be an effective tool for wind farm management and decision-making.

Suggested Citation

  • Tonglin Fu & Chen Wang, 2018. "A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model," Sustainability, MDPI, vol. 10(11), pages 1-24, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3913-:d:178782
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    References listed on IDEAS

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    Cited by:

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    2. Tonglin Fu & Xinrong Li, 2020. "A Combination Forecasting Strategy for Precipitation, Temperature and Wind Speed in the Southeastern Margin of the Tengger Desert," Sustainability, MDPI, vol. 12(4), pages 1-22, February.
    3. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
    4. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    5. Jiang, Ping & Wang, Biao & Li, Hongmin & Lu, Haiyan, 2019. "Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting," Energy, Elsevier, vol. 173(C), pages 468-482.
    6. Youming Cai & Zheng Li & Xu Cai, 2020. "Optimal Inertia Reserve and Inertia Control Strategy for Wind Farms," Energies, MDPI, vol. 13(5), pages 1-16, March.
    7. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    8. Mohammadali Kiehbadroudinezhad & Adel Merabet & Homa Hosseinzadeh-Bandbafha, 2022. "Review of Latest Advances and Prospects of Energy Storage Systems: Considering Economic, Reliability, Sizing, and Environmental Impacts Approach," Clean Technol., MDPI, vol. 4(2), pages 1-25, June.
    9. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.

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