A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/21/8107 |
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| author | Manisha Sawant Rupali Patil Tanmay Shikhare Shreyas Nagle Sakshi Chavan Shivang Negi Neeraj Dhanraj Bokde |
| author_facet | Manisha Sawant Rupali Patil Tanmay Shikhare Shreyas Nagle Sakshi Chavan Shivang Negi Neeraj Dhanraj Bokde |
| author_sort | Manisha Sawant |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:06:22Z |
| format | Article |
| id | doaj.art-5ff709228fa241d7b06bf3e415bca1c0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T19:06:22Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5ff709228fa241d7b06bf3e415bca1c02023-11-24T04:31:56ZengMDPI AGEnergies1996-10732022-10-011521810710.3390/en15218107A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power PredictionManisha Sawant0Rupali Patil1Tanmay Shikhare2Shreyas Nagle3Sakshi Chavan4Shivang Negi5Neeraj Dhanraj Bokde6Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Nagpur 441108, IndiaDepartment of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, IndiaDepartment of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, IndiaDepartment of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, IndiaDepartment of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, IndiaDepartment of Electronics and Telcommunication Engineering, K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai 400077, IndiaCenter for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, DenmarkWith 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.https://www.mdpi.com/1996-1073/15/21/8107wind power predictionmachine learningdeep learninghybrid methodstime series analysis |
| spellingShingle | Manisha Sawant Rupali Patil Tanmay Shikhare Shreyas Nagle Sakshi Chavan Shivang Negi Neeraj Dhanraj Bokde A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction Energies wind power prediction machine learning deep learning hybrid methods time series analysis |
| title | A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction |
| title_full | A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction |
| title_fullStr | A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction |
| title_full_unstemmed | A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction |
| title_short | A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction |
| title_sort | selective review on recent advancements in long short and ultra short term wind power prediction |
| topic | wind power prediction machine learning deep learning hybrid methods time series analysis |
| url | https://www.mdpi.com/1996-1073/15/21/8107 |
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