Noman Shabbir1, Lauri Kütt1, Muhammad Jawad2, Oleksandr Husev1, Ateeq Ur Rehman3, Akber Abid Gardezi4, Muhammad Shafiq5, Jin-Ghoo Choi5,*
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1017-1033, 2022, DOI:10.32604/cmc.2022.024576
Abstract Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term wind energy forecasting problem in… More >
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