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

Mingxiang Li, Tianyi Zhang, Haizhu Yang and Kun Liu ()
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Mingxiang Li: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
Tianyi Zhang: School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China
Haizhu Yang: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
Kun Liu: Tianjin Eco-Environmental Monitoring Center, Tianjin 300191, China

Energies, 2024, vol. 17, issue 20, 1-16

Abstract: 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.

Keywords: multiple load forecasting; maximum information coefficient; temporal convolutional neural network; Informer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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