Short-Term Electricity Load Forecasting in Power Systems Using BO-VMD-STGCN
Main Article Content
Abstract
To address the strong nonlinearity, multi-fluctuation coupling, and spatio-temporal correlation characteristics inherent in short-term power load, a forecasting model based on BO-VMD-STGCN (bayesian optimization, variational mode decomposition and spatio-temporal graph convolutional networks) is proposed. Firstly, BO is utilized for the adaptive tuning of key parameters in VMD, thereby achieving an optimal multi-scale decomposition of the original load sequence and mitigating data non-stationarity. Subsequently, the decomposed modal components along with the reconstructed sequences are fed into the STGCN, wherein a stacking mechanism with residual connections is incorporated to synergistically extract temporal dependencies and spatial correlations via spatio-temporal convolutions. Experimental results demonstrate that the proposed model achieves an MAPE, RMSE, and MAE of 2.84%, 62.68, and 38.36, respectively, along with an R2 of 0.92. Compared with LSTM, STGCN, STM-GCN, and GCN-LSTM, the R2 metric is improved by 26.39%, 19.73%, 18.42%, and 35.29%, respectively. Exhibiting marked superiority over benchmark approaches in both goodness-of-fit and predictive fidelity, the proposed model underscores its intrinsic strengths in jointly capturing spatio-temporal dependencies and adaptively disentangling non-stationary temporal dynamics. It thus establishes a reliable, accurate, and interpretable paradigm for short-term electricity load forecasting.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
M. Tan, S. Yuan, S. Li, et al., “Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning,” IEEE transactions on power systems, vol. 35, no. 4, pp. 2937–2948, 2019.
S. Li, W. Kong, Y. Wang, et al., “Medium and long-term energy demand forecasts by sectors in China under the goal of “carbon peaking & carbon neutrality”: Based on the LEAP-China model,” Energy, vol. 310, Art. no. 133017, 2024.
M. Tan, C. Hu, J. Chen, et al., “Multi-node load forecasting based on multi-task learning with modal feature extraction,” Engineering applications of artificial intelligence, vol. 112, Art. no. 104856, 2022.
Q. Dong, R. Huang, C. Cui, et al., “Short-Term Electricity-Load Forecasting by deep learning: A comprehensive survey,” Engineering Applications of Artificial Intelligence, vol. 154, Art. no. 110980, 2025.
J. W. Taylor, “Short-term electricity demand forecasting using double seasonal exponential smoothing,” Journal of the Operational Research Society, vol. 54, no. 8, pp. 799–805, 2003.
G. Dudek, “Pattern-based local linear regression models for short-term load forecasting,” Electric power systems research, vol. 130, pp. 139–147, 2016.
S. S. Pappas, L. Ekonomou, D. C. Karamousantas, et al., “Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models,” Energy, vol. 33, no. 9, pp. 1353–1360, 2008.
H. Al-Hamadi and S. Soliman, “Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model,” Electric power systems research, vol. 68, no. 1, pp. 47–59, 2004.
S. Smyl, G. Dudek, and P. Pełka, “ES-dRNN: A hybrid exponential smoothing and dilated recurrent neural network model for short-term load forecasting,” IEEE transactions on neural networks and learning systems, vol. 35, no. 8, pp. 11346–11358, 2023.
Y. Liang, D. Niu, and W.-C. Hong, “Short term load forecasting based on feature extraction and improved general regression neural network model,” Energy, vol. 166, pp. 653–663, 2019.
K. Alhashemi and O. T. Altinoz, “Power Demand Forecasting in Iraq Using Singular Spectrum Analysis and Kalman Filter-Smoother,” IEEE Access, 2025.
N. Huang, G. Lu, and D. Xu, “A permutation importance-based feature selection method for short-term electricity load forecasting using random forest,” Energies, vol. 9, no. 10, Art. no. 767, 2016.
M. Krichen, “Convolutional neural networks: A survey,” Computers, vol. 12, no. 8, Art. no. 151, 2023.
P. Ran, K. Dong, X. Liu, et al., “Short-term load forecasting based on CEEMDAN and Transformer,” Electric Power Systems Research, vol. 214, Art. no. 108885, 2023.
C. Cai, Y. Li, Z. Su, et al., “Short-term electrical load forecasting based on VMD and GRU-TCN hybrid network,” Applied Sciences, vol. 12, no. 13, Art. no. 6647, 2022.
X. Gao, X. Li, B. Zhao, et al., “Short-term electricity load forecasting model based on EMD-GRU with feature selection,” Energies, vol. 12, no. 6, Art. no. 1140, 2019.
P. C. Huy, N. Q. Minh, N. D. Tien, et al., “Short-term electricity load forecasting based on temporal fusion transformer model,” Ieee Access, vol. 10, pp. 106296–106304, 2022.
M. Liu, H. Qin, R. Cao, et al., “Short-term load forecasting based on improved TCN and DenseNet,” IEEE Access, vol. 10, pp. 115945–115957, 2022.
C. Zuo, J. Wang, M. Liu, et al., “An ensemble framework for short-term load forecasting based on TimesNet and TCN,” Energies, vol. 16, no. 14, Art. no. 5330, 2023.
M. Wei, M. Wen, and Y. Zhang, “A novel spatial electric load forecasting method based on LDTW and GCN,” IET Generation, Transmission & Distribution, vol. 18, no. 3, pp. 491–505, 2024.
Z. Tan, C. Ge, and Z. Pan, “Short-term spatial load forecasting method based on spatio-temporal graph convolutional network,” in 2024 4th International Conference on New Energy and Power Engineering (ICNEPE), 2024:286–291.
Z. Li, F. Liu, W. Yang, et al., “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE transactions on neural networks and learning systems, vol. 33, no. 12, pp. 6999–7019, 2021.
B. Wang, Y. Tang, Y. Huang, et al., “Power system emergency control strategy based on severely disturbed units identification and STGCN-DDQN,” Electric Power Systems Research, vol. 226, Art. no. 109903, 2024.
Y. Jiang, P. Dai, P. Fang, et al., “Electrical-STGCN: An electrical spatio-temporal graph convolutional network for intelligent predictive maintenance,” IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 8509–8518, 2022.
K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE transactions on signal processing, vol. 62, no. 3, pp. 531–544, 2013.
H. Li, T. Liu, X. Wu, et al., “An optimized VMD method and its applications in bearing fault diagnosis,” Measurement, vol. 166, Art. no. 108185, 2020.
C. K. Williams and D. Barber, “Bayesian classification with Gaussian processes,” IEEE Transactions on pattern analysis and machine intelligence, vol. 20, no. 12, pp. 1342–1351, 1998.
Z. Chen, S. Mak, and C. J. Wu, “A hierarchical expected improvement method for Bayesian optimization,” Journal of the American Statistical Association, vol. 119, no. 546, pp. 1619–1632, 2024.
C. Hall, “Natural cubic and bicubic spline interpolation,” SIAM Journal on Numerical Analysis, vol. 10, no. 6, pp. 1055–1060, 1973.
J.-H. Cho, W. Song, H. Choi, et al., “Hole filling method for depth image based rendering based on boundary decision,” IEEE Signal Processing Letters, vol. 24, no. 3, pp. 329–333, 2017.
I. Izonin, R. Tkachenko, N. Shakhovska, et al., “A two-step data normalization approach for improving classification accuracy in the medical diagnosis domain,” Mathematics, vol. 10, no. 11, Art. no. 1942, 2022.
H. Chen, M. Zhu, X. Hu, et al., “Research on short-term load forecasting of new-type power system based on GCN-LSTM considering multiple influencing factors,” Energy Reports, vol. 9, pp. 1022–1031, 2023.
Y. Hu, X. Cheng, S. Wang, et al., “Times series forecasting for urban building energy consumption based on graph convolutional network,” Applied Energy, vol. 307, Art. no. 118231, 2022.
S. Zhang, S. Zhang, J. James, et al., “ST-AGNet: Dynamic power system state prediction with spatial–temporal attention graph-based network,” Applied Energy, vol. 365, Art. no. 123252, 2024.
J. Lin, J. Ma, J. Zhu, et al., “Short-term load forecasting based on LSTM networks considering attention mechanism,” International Journal of Electrical Power & Energy Systems, vol. 137, Art. no. 107818, 2022.