A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction

Manisha Sawant, Rupali Patil, Tanmay Shikhare, Shreyas Nagle, Sakshi Chavan, Shivang Negi and Neeraj Dhanraj Bokde ()
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Manisha Sawant: Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Nagpur 441108, India
Rupali Patil: Department of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, India
Tanmay Shikhare: Department of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, India
Shreyas Nagle: Department of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, India
Sakshi Chavan: Department of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, India
Shivang Negi: Department of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, India
Neeraj Dhanraj Bokde: Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark

Energies, 2022, vol. 15, issue 21, 1-24

Abstract: With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic behaviour of wind, accurate forecasting of wind power for different intervals of time becomes more challenging. Forecasting of wind power generation over different time spans is essential for different applications of wind energy. Recent development in this research field displays a wide spectrum of wind power prediction methods covering different prediction horizons. A detailed review of recent research achievements, performance, and information about possible future scope is presented in this article. This paper systematically reviews long term, short term and ultra short term wind power prediction methods. Each category of forecasting methods is further classified into four subclasses and a comparative analysis is presented. This study also provides discussions of recent development trends, performance analysis and future recommendations.

Keywords: wind power prediction; machine learning; deep learning; hybrid methods; time series analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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