A comprehensive review on deep learning approaches in wind forecasting applications

Zhou Wu, Gan Luo, Zhile Yang, Yuanjun Guo, Kang Li, Yusheng Xue
2022 CAAI Transactions on Intelligence Technology  
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
more » ... w 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 timeseries-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. K E Y W O R D S deep learning, deep neural networks, learning (artificial intelligence) This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
doi:10.1049/cit2.12076 fatcat:mr67mk2t3ndhroizkejcxon4ve