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Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine

Author

Listed:
  • Rui Wang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Jingrui Li

    (School of Accounting, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Jianzhou Wang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Chengze Gao

    (School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China)

Abstract

Accurate wind speed forecasting plays a significant role for grid operators and the use of wind energy, which helps meet increasing energy needs and improve the energy structure. However, choosing an accurate forecasting system is a challenging task. Many studies have been carried out in recent years, but unfortunately, these studies ignore the importance of data preprocessing and the influence of numerous missing values, leading to poor forecasting performance. In this paper, a hybrid forecasting system based on data preprocessing and an Extreme Learning Machine optimized by the cuckoo algorithm is proposed, which can overcome the limitations of the single ELM model. In the system, the standard genetic algorithm is added to reduce the dimensions of the input and utilize the time series model for error correction by focusing on the optimized extreme learning machine model. And according to screened results, the 5% fractile and 95% fractile are applied to compose the upper and lower bounds of the confidence interval, respectively. The assessment results indicate that the hybrid system successfully overcomes some limitations of the single Extreme Learning Machine model and traditional BP and Mycielski models and can be an effective tool compared to traditional forecasting models.

Suggested Citation

  • Rui Wang & Jingrui Li & Jianzhou Wang & Chengze Gao, 2018. "Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine," Energies, MDPI, vol. 11(7), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1712-:d:155525
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    References listed on IDEAS

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