Enhancing the accuracy of electricity consumption forecasting is essential for the safe and stable operation of power systems and the strategic planning of grid companies. To achieve high-precision short-term electricity consumption forecasting, this paper proposes a VMD-xLSTM forecasting model that combines Variational Modal Decomposition (VMD) with an extended Long Short-Term Memory network (xLSTM). This novel hybird model firstly uses the VMD decomposition algorithm to decompose the original electricity consumption time series into multiple modal components and a residual component. Subsequently, leveraging the powerful long-term dependency capturing capabilities of the xLSTM neural network, each decomposed component is independently established the model and forecasted. Finally, the prediction results of each component are superimposed to reconstruct the overall predicted value of electricity consumption. Experimental results demonstrate that the proposed VMD-xLSTM model performs outstandingly, with a Symmetric Mean Absolute Percentage Error (SMAPE) of only 0.86%. This is notably lower than that of the traditional Long Short-Term Memory network (LSTM) at 2.33%, the standalone xLSTM model at 2.15%, and the VMD-LSTM model at 0.90%. Additionally, all other prediction error evaluation metrics of this model are comprehensively superior to the compared models, clearly verifying the VMD-xLSTM model's effectiveness in short-term electricity consumption forecasting tasks through comparative experiments.
| Published in | Automation, Control and Intelligent Systems (Volume 13, Issue 3) |
| DOI | 10.11648/j.acis.20251303.16 |
| Page(s) | 131-139 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Electricity Consumption Forecasting, Extended Long Short-Term Memory Network, Variational Modal Decomposition
| [1] | Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting [J]. Journal of Donghua University (English Edition), 2019, 36(1): 67-76. |
| [2] | Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 1998, 454(1971): 903-995. |
| [3] | Dragomiretskiy K, Zosso D. Variational mode decomposition [J]. IEEE transactions on signal processing, 2013, 62(3): 531-544. |
| [4] | RAMOS P, SANTOS N, RUI R. Performance of state space and ARIMA models for consumer retail sales forecasting [J]. Robotics and Computer-Integrated Manufacturing, 2015, 34: 151-163. |
| [5] | Liu Xiaolei, Lin Zi, Feng Ziming. Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM [J]. Energy, 2021, 227: 1-12. |
| [6] | Huang Nantian, Wang Wenting, Wang Sining, Wang Jun, Cai Guowei, Zhang Liang. Incorporating Load Fluctuation in Feature Importance Profile Clustering for Day-Ahead Aggregated Residential Load Forecasting [J]. IEEE Access, 2020, 8: 25198-25209. |
| [7] | PAPAIOANNOU, GEORGE P., DIKAIAKOS, CHRISTOS, DRAMOUNTANIS, ANARGYROS, et al. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market [J]. Energies, 2016, 9(8): 635. |
| [8] | YU, LEAN, XU, HUIJUAN, TANG, LING. LSSVR ensemble learning with uncertain parameters for crude oil price forecasting [J]. Applied Soft Computing, 2017, 56692-701. |
| [9] | G. Petneházi. Recurrent Neural Networks for Time Series Forecasting. arXiv, Dec. 31, 2018. |
| [10] | HOCHREITER S, SCHMIDHUBER J. Long Short-Term Memory [J]. Neural Computation, 1997, 9(8): 1735-1780. |
| [11] | CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder decoder for statistical machine translation [J]. arXiv: 1406. 1078, 2014. |
| [12] | JIAWEN TIAN, SHUQIANG WANG. Tool wear prediction based on xLSTM combined with the ECA attention mechanism model [J]. Journal of Mechanical Science and Technology, 2025, 39(2): 519-530. |
| [13] | Beck, M., Pöppel, K., Spanring, M., et al. xLSTM: Extended Long Short-Term Memory. 2024, arXiv preprint arXiv: 2405.04517. |
| [14] | Lalitpat A, Warut P, Jirachai B, et al. A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting [J]. Energies, 2023, 16(4): 1860-1860. |
| [15] | Hu Z, Gao Y, Sun L, et al. A novel attention-enhanced LLM approach for accurate power demand and generation forecasting [J]. Renewable Energy, 2025, 252123465-123465. |
APA Style
Zhi, C., Zhong-kuan, H., Chao, X., Yu, S., Lin, L., et al. (2025). Research on Short-Term Electricity Consumption Forecasting Based on VMD-xLSTM. Automation, Control and Intelligent Systems, 13(3), 131-139. https://doi.org/10.11648/j.acis.20251303.16
ACS Style
Zhi, C.; Zhong-kuan, H.; Chao, X.; Yu, S.; Lin, L., et al. Research on Short-Term Electricity Consumption Forecasting Based on VMD-xLSTM. Autom. Control Intell. Syst. 2025, 13(3), 131-139. doi: 10.11648/j.acis.20251303.16
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Download@article{10.11648/j.acis.20251303.16,
author = {Chen Zhi and Han Zhong-kuan and Xun Chao and Shen Yu and Liu Lin and Chen Yan-tao and Zeng Shuai},
title = {Research on Short-Term Electricity Consumption Forecasting Based on VMD-xLSTM
},
journal = {Automation, Control and Intelligent Systems},
volume = {13},
number = {3},
pages = {131-139},
doi = {10.11648/j.acis.20251303.16},
url = {https://doi.org/10.11648/j.acis.20251303.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20251303.16},
abstract = {Enhancing the accuracy of electricity consumption forecasting is essential for the safe and stable operation of power systems and the strategic planning of grid companies. To achieve high-precision short-term electricity consumption forecasting, this paper proposes a VMD-xLSTM forecasting model that combines Variational Modal Decomposition (VMD) with an extended Long Short-Term Memory network (xLSTM). This novel hybird model firstly uses the VMD decomposition algorithm to decompose the original electricity consumption time series into multiple modal components and a residual component. Subsequently, leveraging the powerful long-term dependency capturing capabilities of the xLSTM neural network, each decomposed component is independently established the model and forecasted. Finally, the prediction results of each component are superimposed to reconstruct the overall predicted value of electricity consumption. Experimental results demonstrate that the proposed VMD-xLSTM model performs outstandingly, with a Symmetric Mean Absolute Percentage Error (SMAPE) of only 0.86%. This is notably lower than that of the traditional Long Short-Term Memory network (LSTM) at 2.33%, the standalone xLSTM model at 2.15%, and the VMD-LSTM model at 0.90%. Additionally, all other prediction error evaluation metrics of this model are comprehensively superior to the compared models, clearly verifying the VMD-xLSTM model's effectiveness in short-term electricity consumption forecasting tasks through comparative experiments.
},
year = {2025}
}
TY - JOUR T1 - Research on Short-Term Electricity Consumption Forecasting Based on VMD-xLSTM AU - Chen Zhi AU - Han Zhong-kuan AU - Xun Chao AU - Shen Yu AU - Liu Lin AU - Chen Yan-tao AU - Zeng Shuai Y1 - 2025/09/25 PY - 2025 N1 - https://doi.org/10.11648/j.acis.20251303.16 DO - 10.11648/j.acis.20251303.16 T2 - Automation, Control and Intelligent Systems JF - Automation, Control and Intelligent Systems JO - Automation, Control and Intelligent Systems SP - 131 EP - 139 PB - Science Publishing Group SN - 2328-5591 UR - https://doi.org/10.11648/j.acis.20251303.16 AB - Enhancing the accuracy of electricity consumption forecasting is essential for the safe and stable operation of power systems and the strategic planning of grid companies. To achieve high-precision short-term electricity consumption forecasting, this paper proposes a VMD-xLSTM forecasting model that combines Variational Modal Decomposition (VMD) with an extended Long Short-Term Memory network (xLSTM). This novel hybird model firstly uses the VMD decomposition algorithm to decompose the original electricity consumption time series into multiple modal components and a residual component. Subsequently, leveraging the powerful long-term dependency capturing capabilities of the xLSTM neural network, each decomposed component is independently established the model and forecasted. Finally, the prediction results of each component are superimposed to reconstruct the overall predicted value of electricity consumption. Experimental results demonstrate that the proposed VMD-xLSTM model performs outstandingly, with a Symmetric Mean Absolute Percentage Error (SMAPE) of only 0.86%. This is notably lower than that of the traditional Long Short-Term Memory network (LSTM) at 2.33%, the standalone xLSTM model at 2.15%, and the VMD-LSTM model at 0.90%. Additionally, all other prediction error evaluation metrics of this model are comprehensively superior to the compared models, clearly verifying the VMD-xLSTM model's effectiveness in short-term electricity consumption forecasting tasks through comparative experiments. VL - 13 IS - 3 ER -
State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
Institute of Economic and Technology, State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
Institute of Economic and Technology, State Grid Fujian Electric Power Co., Ltd., Fuzhou, China
Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, China
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