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Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting

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  • Jiang, Ping
  • Wang, Biao
  • Li, Hongmin
  • Lu, Haiyan

Abstract

Wind-speed forecasting plays a crucial part in improving the operational efficiency of wind power generation. However, accurate forecasts are difficult owing to the uncertainty of the wind speed. Although numerous investigations of wind-speed forecasting have been performed, many of the previous studies used wind-speed data directly to make forecasts, which were rarely based on the structural characteristics of the data. Therefore, in this study, a hybrid linear-nonlinear modeling method based on the chaos theory was successfully employed to capture the linear and nonlinear factors hidden in chaotic time series. Before the forecast, the noise in the data was removed using a decomposition algorithm. Then, through the phase-space reconstruction, the one-dimensional time series were extended to the multi-dimensional space to determine the utilization form of the data. Finally, Holt's exponential smoothing based on the firefly optimization algorithm and support vector regression were combined to predict the wind speed. The experimental results show that the proposed model is not only better than the comparison models but also has great application potential in the wind power generation system.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:173:y:2019:i:c:p:468-482
    DOI: 10.1016/j.energy.2019.02.080
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    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    3. Sun, Shaolong & Qiao, Han & Wei, Yunjie & Wang, Shouyang, 2017. "A new dynamic integrated approach for wind speed forecasting," Applied Energy, Elsevier, vol. 197(C), pages 151-162.
    4. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    5. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    6. Li, Hongmin & Wang, Jianzhou & Lu, Haiyan & Guo, Zhenhai, 2018. "Research and application of a combined model based on variable weight for short term wind speed forecasting," Renewable Energy, Elsevier, vol. 116(PA), pages 669-684.
    7. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    8. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    9. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    10. 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.
    11. 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.
    12. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    13. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Ma, Ping, 2017. "A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction," Energy, Elsevier, vol. 138(C), pages 977-990.
    14. Wang, Jianzhou & Heng, Jiani & Xiao, Liye & Wang, Chen, 2017. "Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting," Energy, Elsevier, vol. 125(C), pages 591-613.
    15. Wang, Yamin & Wu, Lei, 2016. "On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation," Energy, Elsevier, vol. 112(C), pages 208-220.
    16. Liu, Da & Wang, Jilong & Wang, Hui, 2015. "Short-term wind speed forecasting based on spectral clustering and optimised echo state networks," Renewable Energy, Elsevier, vol. 78(C), pages 599-608.
    17. Ackermann, Thomas & Söder, Lennart, 2000. "Wind energy technology and current status: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 4(4), pages 315-374, December.
    18. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    19. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    20. 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.
    21. 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.
    22. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
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