New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight
Erlong Zhao, Shaolong Sun, Shouyang Wang
Abstract
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.