Wind Speed Forecasting Using a Two-Stage Forecasting System With an Error Correcting and Nonlinear Ensemble Strategy

• Published in: IEEE access Vol. 7; pp. 176000 - 176023
• Main Authors: Zhang, Lifang, Dong, Yao, Wang, Jianzhou
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Energy utilization; ensemble strategy; Error correction; Forecasting; forecasting accuracy; Mathematical models; multi-objective optimization algorithm; Multiple objective analysis; Optimization; Predictive models; Stability; Stability analysis; Wind energy; Wind power; Wind speed; Wind speed forecasting
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2957174

Precise wind speed prediction is increasingly practical for sustained and stable wind energy utilization considering the growing portion of wind energy in the global electric grid. Although plenty of wind speed forecasting approaches have been devoted to improving forecasting performance, the majority have neglected the utilization of error information, integrated the forecasting value of every component with simple ensemble approaches, and ignored forecasting stability, which may make the forecasting results poor. Considering the above drawbacks, a two-stage forecasting system is performed in our study based on the data preprocessing approach, improved multi-objective optimization algorithm, error correction and nonlinear ensemble strategy. The developed system effectively overcomes the shortcomings of previous models and observably prompts wind speed forecasting capacity. For investigating the prediction capacity of the developed system, six wind speed series obtained from China and Spain are applied as case sites. The forecasting consequences indicate that the developed system is more conducive to enhancing forecasting precision and stability than other involved models, which can provide beneficial assistance for wind speed forecasting.