Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer

被引:1
|
作者
Li, Mingxiang [1 ]
Zhang, Tianyi [2 ]
Yang, Haizhu [1 ]
Liu, Kun [3 ]
机构
[1] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo 454003, Peoples R China
[2] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China
[3] Tianjin Ecoenvironm Monitoring Ctr, Tianjin 300191, Peoples R China
关键词
multiple load forecasting; maximum information coefficient; temporal convolutional neural network; Informer;
D O I
10.3390/en17205181
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction.
引用
收藏
页数:15
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