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METHODS article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1419549
This article is part of the Research Topic Urban Energy System Planning, Operation, and Control with High Efficiency and Low Carbon Goals View all 23 articles

Study on mining wind information for identifying potential offshore wind farms using deep learning

Provisionally accepted
Jiahui Zhang Jiahui Zhang 1*Tao Zhang Tao Zhang 2Yixuan Li Yixuan Li 1Xiang Bai Xiang Bai 1Longwen Chang Longwen Chang 3
  • 1 Shanxi Energy Internet Research Institute, Taiyuan, China
  • 2 Shanghai Zhongyuan Network Technology Co., Ltd, Shanghai, China
  • 3 Taiyuan University of Technology, Taiyuan, Shanxi Province, China

The final, formatted version of the article will be published soon.

    The global energy demand is increasing due to climate changes and carbon usages. Accumulating evidences showed energy sources using offshore wind from the sea can be added to increase our consumption capacity in long term. In addition, building offshore wind farms can also be environmentally advantageous compared to onshore farms. The assessment of wind energy resources is crucial for the site selection of wind farms. Currently, short-term wind forecast models have been developed to predict the wind power generation. However, methods are needed to improve the forecasting accuracy for ever-changing weather data. So, we try to use deep learning methods to predict long-term wind energy for identifying potential offshore wind farms. The experimental results indicate that PredRNN++ prediction model designed from the spatiotemporal perspective is feasible to evaluate long-term wind energy resources and has better performance than traditional LSTM.

    Keywords: Offshore wind energy, Long-term wind resources prediction, Spatiotemporal prediction; Deep learning methods, predRNN++ model, Offshore wind farms

    Received: 18 Apr 2024; Accepted: 18 Jul 2024.

    Copyright: © 2024 Zhang, Zhang, Li, Bai and Chang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jiahui Zhang, Shanxi Energy Internet Research Institute, Taiyuan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.