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A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy

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  • Yuanzhuo Du

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Kun Zhang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Qianzhi Shao

    (Industrial Branch, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110004, China)

  • Zhe Chen

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

Wind power generation is a type of renewable energy that has the advantages of being pollution-free and having a wide distribution. Due to the non-stationary characteristics of wind power caused by atmospheric chaos and the existence of outliers, the prediction effect of wind power needs to be improved. Therefore, this study proposes a novel hybrid prediction method that includes data correlation analyses, power decomposition and reconstruction, and novel prediction models. The Pearson correlation coefficient is used in the model to analyze the effects between meteorological information and power. Furthermore, the power is decomposed into different sub-models by ensemble empirical mode decomposition. Sample entropy extracts the correlations among the different sub-models. Meanwhile, a long short-term memory model with an asymmetric error loss function is constructed considering outliers in the power data. Wind power is obtained by stacking the predicted values of subsequences. In the analysis, compared with other methods, the proposed method shows good performance in all cases.

Suggested Citation

  • Yuanzhuo Du & Kun Zhang & Qianzhi Shao & Zhe Chen, 2023. "A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6285-:d:1117175
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