Data-driven wind power prediction model based on improved generative adversarial network
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Zhang Peng
Abstract
Accurate and timely wind power forecast is difficult because of the volatility and intermittency of wind energy, which are strongly influenced by meteorological conditions. In order to solve this problem, this research suggests a data-driven wind power prediction technique based on Elman neural networks and an enhanced generative adversarial network (GAN). First, outliers in the supervisory control and data acquisition (SCADA) power and wind speed data are found by combining the methods of density clustering and regression threshold truncation. To guarantee the temporal continuity of wind power data with the original characteristics, minority class samples are then generated using an enhanced GAN and added to the dataset. In order to obtain an accurate wind power prediction, the balanced data is then fed into a prediction model that combines the ABC algorithm and Elman neural networks. The fitness value is the mean squared error (MSE) of the training data, and the optimization objective is the connection weights of the Elman neural network. Results from experiments show that the suggested model outperforms earlier state-of-the-art techniques and significantly increases wind power prediction accuracy. It also has the advantages of high stability and quick convergence speed, and it can capture the long-term dependencies of wind power data.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors contribute to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: Authors do not have any conflicts.
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Research funding: The authors did not receive any funding.
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Data availability: No datasets were generated or analyzed during the current study.
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Code availability: Not applicable.
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