Wu, Z, Luo, G, Yang, Z et al. (3 more authors) (2022) A comprehensive review on deep learning approaches in wind forecasting applications. CAAI Transactions on Intelligence Technology, 7 (2). pp. 129-143. ISSN 2468-2322
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. 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. |
Keywords: | deep learning; deep neural networks; learning (artificial intelligence) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Nov 2021 12:27 |
Last Modified: | 04 Sep 2023 15:20 |
Status: | Published |
Publisher: | Wiley Open Access |
Identification Number: | 10.1049/cit2.12076 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180409 |