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Short-term Wind Power Prediction based on Combined LSTM
  • +2
  • Zhao Yuyang Zhao,
  • Li Lincong Li,
  • Guo Yingjun Guo,
  • Shi Boming Shi,
  • Sun Hexu Sun
Zhao Yuyang Zhao
Hebei University of Science and Technology
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Li Lincong Li
Hebei University of Science and Technology
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Guo Yingjun Guo
Hebei University of Science and Technology
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Shi Boming Shi
Hebei University of Science and Technology
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Sun Hexu Sun
Hebei University of Science and Technology

Corresponding Author:[email protected]

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Abstract

Wind power is an exceptionally clean source of energy, its rational utilization can fundamentally alleviate the energy, environment, and development problems, especially under the goals of “carbon peak” and “carbon neutrality”. A combined short-term wind power prediction based on LSTM artificial neural network has been studied aiming at the nonlinearity and volatility of wind energy. Due to the large amount of historical data required to predict the wind power precisely, the ambient temperature and wind speed, direction, and power are selected as model input. The CEEMDAN has been introduced as data preprocessing to decomposes wind power data and reduce the noise. And the PSO is conducted to optimize the LSTM network parameters. The combined prediction model with high accuracy for different sampling intervals has been verified by the wind farm data of Chongli Demonstration Project in Hebei Province. The results illustrate that the algorithm can effectively overcome the abnormal data influence and wind power volatility, thereby provide a theoretical reference for precise short-term wind power prediction.
31 May 2023Submitted to IET Generation, Transmission & Distribution
02 Jun 2023Submission Checks Completed
02 Jun 2023Assigned to Editor
03 Jun 2023Reviewer(s) Assigned
16 Jun 2023Review(s) Completed, Editorial Evaluation Pending
19 Jun 2023Editorial Decision: Revise Major
13 Jul 20231st Revision Received
14 Jul 2023Submission Checks Completed
14 Jul 2023Assigned to Editor
21 Jul 2023Reviewer(s) Assigned
04 Aug 2023Review(s) Completed, Editorial Evaluation Pending
15 Aug 2023Editorial Decision: Revise Major
03 Sep 20232nd Revision Received
05 Sep 2023Submission Checks Completed
05 Sep 2023Assigned to Editor
05 Sep 2023Review(s) Completed, Editorial Evaluation Pending
07 Sep 2023Reviewer(s) Assigned
20 Sep 2023Editorial Decision: Accept