TY - JOUR
T1 - New developments in wind energy forecasting with artificial intelligence and big data
T2 - a scientometric insight
AU - Zhao, Erlong
AU - Sun, Shaolong
AU - Wang, Shouyang
N1 - Publisher Copyright:
© 2022 Xi'an Jiaotong University
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Big data analytics
KW - Forecasting methods
KW - Wind energy
UR - https://www.scopus.com/pages/publications/85133414144
U2 - 10.1016/j.dsm.2022.05.002
DO - 10.1016/j.dsm.2022.05.002
M3 - 文献综述
AN - SCOPUS:85133414144
SN - 2666-7649
VL - 5
SP - 84
EP - 95
JO - Data Science and Management
JF - Data Science and Management
IS - 2
ER -