Short-term Load Forecasting Based on Dual-attention Temporal Convolutional Long Short-term Memory Network

Authors

Lifen Li, Department of Computer, North China Electric Power University, Baoding 071000, China; 2. Department of Electronic & Communication Engineering, North China Electric Power University, Baoding 071000, China; Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding 071000, China
Jinyue Zhang, Department of Computer, North China Electric Power University, Baoding 071000, China; 2. Department of Electronic & Communication Engineering, North China Electric Power University, Baoding 071000, China; Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding 071000, China
Wangbin Cao, Department of Electronic & Communication Engineering, North China Electric Power University, Baoding 071000, China; Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071000, China
Huawei Mei, Department of Computer, North China Electric Power University, Baoding 071000, China; Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, Baoding 071000, China

Abstract

Abstract: In order to improve the accuracy of load forecasting and fully extract the hidden relationships between load and other characteristic factors, a load forecasting method based on dual-attention temporal convolutional LSTM network (DA-TCLSNet) was proposed. Correlation analysis was conducted on the dataset using the maximum information coefficient method to perform feature screening to reduce the computational cost of the model. The model input was constructed using a sliding window. The DATCLSNet forecasting model was constructed. The temporal convolutional layer extracted dependencies at different time scales and captured the nonlinear characteristics among variables such as load and weather. The multi-head sparse self-attention layer focused on important information. The LSTM network layer explored the long-term dependency of time series. The temporal pattern attention layer realized adaptive learning of the relationships among different variables at the same time step and connected the above modules through a residual structure to improve the expressive power of the model. Experimental results show that the proposed algorithm offers better forecasting performance than other load forecasting methods.

Recommended Citation

Li, Lifen; Zhang, Jinyue; Cao, Wangbin; and Mei, Huawei (2025) "Short-term Load Forecasting Based on Dual-attention Temporal Convolutional Long Short-term Memory Network," Journal of System Simulation: Vol. 37: Iss. 8, Article 9.
DOI: 10.16182/j.issn1004731x.joss.24-0282
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss8/9

First Page

2004

Last Page

2015

CLC

TP391.9

Recommended Citation

Li Lifen, Zhang Jinyue, Cao Wangbin, et al. Short-term Load Forecasting Based on Dual-attention Temporal Convolutional Long Short-term Memory Network[J]. Journal of System Simulation, 2025, 37(8): 2004-2015.

Corresponding Author

Cao Wangbin

DOI

10.16182/j.issn1004731x.joss.24-0282