Zhang, H, Yue, D, Dou, C et al. (2 more authors) (2022) Two-Step Wind Power Prediction Approach With Improved Complementary Ensemble Empirical Mode Decomposition and Reinforcement Learning. IEEE Systems Journal, 16 (2). pp. 2545-2555. ISSN 1932-8184
Abstract
The strong stochastic nature of wind power generation makes it extremely challenging to accurately predict and support the planning and operation of modern power systems with significant penetration of renewable energy. This article proposes a two-step wind power prediction method, which consists of two phases: long time-scale coarse prediction and short time-scale fine correction. In the long time-scale phase, a complementary ensemble empirical mode decomposition-based sigma point Kalman filter approach is proposed to coarsely predict wind power merely with historical data. In the short time-scale phase, a deep deterministic policy gradient approach learns from real-time weather information to correct the coarse prediction result, which results in an improved prediction accuracy. A real-life case study confirms that the proposed method can properly predict wind power generation and have a better prediction accuracy than existing techniques, thus offering a viable and promising alternative for predicting wind power generation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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: | 21 Apr 2021 10:14 |
Last Modified: | 26 Jul 2022 11:11 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/jsyst.2021.3065566 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173036 |