影响因子：7.8
DOI码：10.1016/j.asoc.2024.111853
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Applied Soft Computing
刊物所在地：荷兰
关键字：Information fusion; Feature extraction; Empirical wavelet transform (EWT); Grey Wolf optimization (GWO); Bi-long-short-term memory (BiLSTM)
摘要：Accurate power load prediction is beneficial to the efficient use of electric energy and the orderly development of power systems. Given the strong volatility and complexity of power load series, a hybrid load forecasting method based on multiscale and mesoscale information fusion, signal decomposition, model optimization, and bi-long-short-term memory (BiLSTM) is proposed. Firstly, the load sequence is analyzed on different time scales, and the extracted multi-scale information and mesoscale information are fused to improve the perception ability. Secondly, the empirical wavelet transform (EWT) with adaptive decomposition ability is used to decompose the sequence and extract the rich feature information. Thirdly, the complexity, volatility, and uncertainty of each mode component were analyzed, the data features were fully mined, and the feature fusion was carried out by the TOPISIS evaluation method. The BiLSTM model and the GWO-BiLSTM model are used to predict the low-frequency component and the high-frequency component, respectively. The optimization of Grey Wolf optimization (GWO) algorithm can improve the BiLSTM model's ability to learn long-term time series. Finally, the analysis of application examples shows that compared with various prediction models, the prediction error of mixed model EWT-SGEO-BiLSTM is the smallest, MAPE is as low as 1.07 %, and goodness of fit R2 is 0.99 which verifies the accuracy and applicability of the intelligent model.
合写作者：李金伟,彭丽玲,Hsin-Pou Huang
第一作者：范国峰
论文类型：期刊论文
通讯作者：洪维强
论文编号：111853
学科门类：工学
文献类型：J
卷号：162
ISSN号：1568-4946
是否译文：否
发表时间：2024-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S1568494624006276?via%3Dihub