影响因子：2.7
DOI码：10.1007/s00202-022-01628-y
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Electrical Engineering
刊物所在地：德国
关键字：Empirical wavelet transform (EWT); Random forest (RF); Short-term load forecasting
摘要：Aiming at the problem of strong randomness and low forecasting accuracy in short-term electric load, a method based on empirical wavelet transform and random forest is proposed. In this method, the noise is extracted and stripped by wavelet transform, and the original data are decomposed into several groups of low or high frequencies. The empirical wavelet transform is more adaptive, and details are demonstrated accurately. These decomposed modes are used as characteristic variables to forecast with random forest. This method has three advantages: (1) Because of the instability of electric data, the empirical wavelet decomposition can be used to characterize the non-stationary signal characteristics. (2) Empirical wavelet transform has more advantages in time domain analysis because of its correlation to signal removal and the tendency of noise whitening. (3) More adaptive details with empirical mode (noise is no exception) are expressed in the random forest, and it still demonstrates accurate results even after data features are lost. The Australian electricity data in different periods are used for case analysis. The results compared with other methods have shown that the model could reveal effectively the influence of random noise and improve the accuracy and reliability of short-term load forecasting.
合写作者：彭丽玲
第一作者：范国峰
论文类型：期刊论文
通讯作者：洪维强
学科门类：工学
文献类型：J
卷号：104
期号：6
页面范围：4433-4449
ISSN号：0948-7921
是否译文：否
发表时间：2022-01-01
收录刊物：SCI
发布期刊链接：https://link.springer.com/article/10.1007/s00202-022-01628-y