离群值
样本熵
风力发电
希尔伯特-黄变换
熵(时间箭头)
期限(时间)
皮尔逊积矩相关系数
风速
相关系数
数学
计算机科学
统计
时间序列
能量(信号处理)
气象学
工程类
物理
电气工程
量子力学
作者
Yan Du,Kun Zhang,Q. T. Shao,Zhe Chen
摘要
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.
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