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References

1 
Hippert, H. S., Pedreira, C. E., & Souza, R. C., “Neural networks for short-term load forecasting: A review and evaluation,” IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 44~55, 2001.DOI
2 
Martin T. Hagan, and Suzanne M. Behr, “The Time Series Approach to Short Term Load Forecasting,” IEEE Transactions on Power Systems, vol. 2, no. 3, pp. 785~791, 1987.DOI
3 
L. J. Soares, M. C. Medeiros, “Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data,” International Journal of Forecasting, vol. 24, no. 4, pp. 630~644, 2008.DOI
4 
Z. Tan, J. Zhang, Y. He, Y. Zhang, G. Xiong, and Y. Liu, “Short-Term Load Forecasting Based on Integration of SVR and Stacking,” IEEE Access, vol. 8, pp. 227719~227728, 2020.DOI
5 
A. T. Chan, J. H. Chow, and C. Y. Tse, “Short-term load forecasting with seasonal effects using random forests,” Energy, vol. 36, no. 3, pp. 1413~1421, 2011.URL
6 
S. K. Kim, M. H. Kim, and Y. J. Kim, “Support vector regression for short-term load forecasting,” IEEE Transactions on Power Systems, vol. 22, no. 2, pp. 576~585, 2007.URL
7 
K. S. Kim, J. H. Lee, and S. B. Hong, “Short-term load forecasting with Gated Recurrent Units (GRUs),” IEEE Transactions on Smart Grid, vol. 12, no. 3, pp. 2201~2210, 2021.URL
8 
Jatin Bedi, Durga Toshniwal, “Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting,” IEEE Access, vol 6, pp. 49144~49156, 2018.DOI
9 
M. S. Hossain, and H. Mahmood, “Short-Term Load Forecasting Using an LSTM Neural Network,” IEEE Power Energy Conference at Illinois(PECI), pp. 1~6, 2020.DOI
10 
Bo-Sung Kwon, Rae-Jun Park, and Kyung-Bin Song, “Short-Term Load Forecasting Based on Deep Neural Networks Using LSTM Layer,” Journal of Electrical Engineering and Technology, vol. 15, pp. 1501~1509, 2020.DOI
11 
S. H. Rafi, N. A. Masood, S. R. Deeba, and E. Hossain, “A Short-Term Load Forecasting Method Using Integrated CNN nad LSTM Network,” IEEE Access, vol. 9, pp. 32436~32448, 2021.DOI
12 
Korea Power Exchange, “A Study on Short-Term Load Forecasting Technique and its Application,” 2011.URL
13 
Minh-Thang Lougn, Hieu Pham, Christoper D. Manning, “Effective Approaches to Attention-based Neural Machine Translation,” Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412~1421, 2015.URL
14 
Xiang Li, Wei Zhang, Qian Ding, “Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism,” Signal Processing, vol. 161, pp. 136~154, 2019.DOI
15 
Lee Jonghyeok, Choi Suyeon, Kim Yeonjoo, “Prediction of dam inflow based on LSTM-s2s model using luong attention,” J. Korea Water Resour Assoc. vol. 55, no. 7, pp. 495~504, 2022.DOI
16 
Kuihua Wu, Jian Wu, Liang Feng, Bo Yang, Rong Liang, Shenquan Yang, Ren Zhao, “An attention-based CNN-LSTM-BiLSTM model for short-term electric load forecasting in integrated energy system,” International Transactions on Electrical Energy Systems, vol. 31, Issue 1, 2020.DOI