An Improved Short-Term Electricity Load Forecasting Method: The VMD–KPCA–xLSTM–Informer Model

Jiawen You, Huafeng Cai (), Dadian Shi and Liwei Guo
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Jiawen You: Normal School of Vocational Techniques, Hubei University of Technology, Wuhan 430068, China
Huafeng Cai: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Dadian Shi: Detroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, China
Liwei Guo: Detroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, China

Energies, 2025, vol. 18, issue 9, 1-19

Abstract: This paper proposes a hybrid forecasting method (VMD–KPCA–xLSTM–Informer) based on variational-mode decomposition (VMD), kernel principal component analysis (KPCA), extended long short-term memory network (xLSTM), and the Informer model. First, the method decomposes the original power load data and environmental parameter data using VMD to capture their multi-scale characteristics. Next, KPCA extracts nonlinear features and reduces the dimensionality of the decomposed modals to eliminate redundant information while retaining key features. The xLSTM network then models temporal dependencies to enhance the model’s memory capability and prediction accuracy. Finally, the Informer model processes long-sequence data to improve prediction efficiency. Experimental results demonstrate that the VMD–KPCA–xLSTM–Informer model achieves an average absolute percentage error (MAPE) as low as 2.432% and a coefficient of determination ( R 2 ) of 0.9532 on dataset I, while, on dataset II, it attains a MAPE of 4.940% and an R 2 of 0.8897. These results confirm that the method significantly improves the accuracy and stability of short-term power load forecasting, providing robust support for power system optimization.

Keywords: short-term electricity load forecasting; variational-mode decomposition; hybrid neural network (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: 2025
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