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Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Author

Listed:
  • Chao Fu

    (College of Engineering, Hebei Normal University, Shijiazhuang 050024, China
    Postdoctoral Mobile Station, the PLA Army Engineering University, Shijiazhuang 050000, China)

  • Guo-Quan Li

    (Tangshan Kailuan Thermal Power Co., Ltd., Tangshan 063000, China)

  • Kuo-Ping Lin

    (Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan)

  • Hui-Juan Zhang

    (School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, China)

Abstract

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

Suggested Citation

  • Chao Fu & Guo-Quan Li & Kuo-Ping Lin & Hui-Juan Zhang, 2019. "Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:512-:d:199090
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    References listed on IDEAS

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    Cited by:

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    2. Hongbo Gao & Shuang Qiu & Jun Fang & Nan Ma & Jiye Wang & Kun Cheng & Hui Wang & Yidong Zhu & Dawei Hu & Hengyu Liu & Jun Wang, 2023. "Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    3. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    4. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
    5. Wei Sun & Qi Gao, 2019. "Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear–Nonlinear Combination Optimization Model," Energies, MDPI, vol. 12(12), pages 1-27, June.
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    7. Wenlong Fu & Kai Wang & Jianzhong Zhou & Yanhe Xu & Jiawen Tan & Tie Chen, 2019. "A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy," Sustainability, MDPI, vol. 11(6), pages 1-24, March.

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