Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism

Guanchen Geng, Yu He, Jing Zhang, Tingxiang Qin and Bin Yang
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Guanchen Geng: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Yu He: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Jing Zhang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Tingxiang Qin: PowerChina Guizhou Engineering Co., Ltd., Guiyang 550001, China
Bin Yang: PowerChina Guizhou Engineering Co., Ltd., Guiyang 550001, China

Energies, 2023, vol. 16, issue 12, 1-13

Abstract: A new prediction framework is proposed to improve short-term power load forecasting accuracy. The framework is based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). The framework follows a two-step process. In the first step, PSO is applied to optimize the VMD decomposition method. The original electricity load sequence is decomposed, and the fitness function uses sample entropy to describe the complexity of the time series. The decomposed sub-sequences are combined with relevant features, such as meteorological data, to form the input sequence of the prediction model. In the second step, TCN is selected as the prediction model, and it is embedded with an attention mechanism to improve prediction accuracy. The above input sequence is fed to the model to obtain the PSO-VMD-TCN-Attention prediction framework. Load datasets and various prediction models validate the PSO-optimized VMD decomposition method and the TCN-Attention prediction model. Simulation results demonstrate that the PSO-optimized VMD decomposition method enhances the model’s prediction accuracy, and the TCN-Attention prediction model outperforms other prediction models in terms of prediction accuracy and ability.

Keywords: variational mode decomposition (VMD); time convolution network (TCN); attention mechanism; short-term load forecasting; particle swarm optimization (PSO) (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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