A comprehensive review on deep learning approaches in wind forecasting applications. Issue 2 (18th January 2022)

• Record Type: Journal Article
• Authors: Wu, Zhou; Luo, Gan; Yang, Zhile; Guo, Yuanjun; Li, Kang; Xue, Yusheng
• Is Part Of: CAAI transactions on intelligence technology. Volume 7:Issue 2(2022)
• Page Start: 129
• Page End: 143
• Publication Date: 2022-01-18
• Subjects: deep learning -- deep neural networks -- learning (artificial intelligence); Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/cit2.12076
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract: The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real‐world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include time‐series‐based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto‐encoder‐based approaches. In addition, future development directions of deep‐learning‐based wind energy forecasting have also been discussed.