New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight
Accurate forecasting results are crucial for increasing energy efficiency and lowering energy consumption in wind energy. Big data and artificial intelligence (AI) have great potential in wind energy forecasting. Although the literature on this subject is extensive, it lacks a comprehensive research...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2022-06-01
|
| Series: | Data Science and Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764922000212 |
| _version_ | 1797976463047131136 |
|---|---|
| author | Erlong Zhao Shaolong Sun Shouyang Wang |
| author_facet | Erlong Zhao Shaolong Sun Shouyang Wang |
| author_sort | Erlong Zhao |
| collection | DOAJ |
| description | Accurate forecasting results are crucial for increasing energy efficiency and lowering energy consumption in wind energy. Big data and artificial intelligence (AI) have great potential in wind energy forecasting. Although the literature on this subject is extensive, it lacks a comprehensive research status survey. In identifying the evolution rules of big data and AI methods in wind energy forecasting, this paper summarizes the studies on big data and AI in wind energy forecasting over the last two decades. The existing big data types, analysis techniques, and forecasting methods are classified and sorted by combining literature reviews and scientometrics methods. Furthermore, the research trend of wind energy forecasting methods is determined based on big data and artificial intelligence by combing the existing research hotspots and frontier progress. Finally, this paper summarizes existing research’s opportunities, challenges, and implications from various perspectives. The research results serve as a foundation for future research and promote the further development of wind energy forecasting. |
| first_indexed | 2024-04-11T04:51:16Z |
| format | Article |
| id | doaj.art-9d1f4de9fb47435f910fa3f5d6984df4 |
| institution | Directory Open Access Journal |
| issn | 2666-7649 |
| language | English |
| last_indexed | 2024-04-11T04:51:16Z |
| publishDate | 2022-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj.art-9d1f4de9fb47435f910fa3f5d6984df42022-12-27T04:38:33ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492022-06-01528495New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insightErlong Zhao0Shaolong Sun1Shouyang Wang2School of Management, Xi’an Jiaotong University, Xi’an, 710049, ChinaSchool of Management, Xi’an Jiaotong University, Xi’an, 710049, China; Corresponding author.Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China; School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China; Center for Forecasting Science, Chinese Academy of Sciences, Beijing, 100190, ChinaAccurate forecasting results are crucial for increasing energy efficiency and lowering energy consumption in wind energy. Big data and artificial intelligence (AI) have great potential in wind energy forecasting. Although the literature on this subject is extensive, it lacks a comprehensive research status survey. In identifying the evolution rules of big data and AI methods in wind energy forecasting, this paper summarizes the studies on big data and AI in wind energy forecasting over the last two decades. The existing big data types, analysis techniques, and forecasting methods are classified and sorted by combining literature reviews and scientometrics methods. Furthermore, the research trend of wind energy forecasting methods is determined based on big data and artificial intelligence by combing the existing research hotspots and frontier progress. Finally, this paper summarizes existing research’s opportunities, challenges, and implications from various perspectives. The research results serve as a foundation for future research and promote the further development of wind energy forecasting.http://www.sciencedirect.com/science/article/pii/S2666764922000212Wind energyArtificial intelligenceBig data analyticsForecasting methods |
| spellingShingle | Erlong Zhao Shaolong Sun Shouyang Wang New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight Data Science and Management Wind energy Artificial intelligence Big data analytics Forecasting methods |
| title | New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight |
| title_full | New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight |
| title_fullStr | New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight |
| title_full_unstemmed | New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight |
| title_short | New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight |
| title_sort | new developments in wind energy forecasting with artificial intelligence and big data a scientometric insight |
| topic | Wind energy Artificial intelligence Big data analytics Forecasting methods |
| url | http://www.sciencedirect.com/science/article/pii/S2666764922000212 |
| work_keys_str_mv | AT erlongzhao newdevelopmentsinwindenergyforecastingwithartificialintelligenceandbigdataascientometricinsight AT shaolongsun newdevelopmentsinwindenergyforecastingwithartificialintelligenceandbigdataascientometricinsight AT shouyangwang newdevelopmentsinwindenergyforecastingwithartificialintelligenceandbigdataascientometricinsight |