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References

1 
KPX, Rules on Operating Electricity Market, 2024.URL
2 
K. B. Song and J. H. Lim, “Short-Term Load Forecasting for the Consecutive Holidays Considering Businesses' Operation Rates of Industries,” The Transactions of the Korean Institute of Electrical Engineers, vol. 62, no. 12, pp. 1657-1660, 2013.DOI
3 
N. S. Lim, S. J. Lee and J. H Lim, “Improvement of the Load Forecasting Accuracy by Reflecting the Operation Rates of Industries on the Consecutive Holiday,” The Transactions of the Korean Institute of Electrical Engineers, vol. 65, no. 07, pp. 1115-1120, 2016.DOI
4 
H. W. Lim, Special Day Maximum Load Forecast Using K-means cluster (Master's Thesis, Uiduk University), 2014.URL
5 
J. Jeon, Deep Learning Based Short Term Load Forecasting Considering Holidays and Special Days (Master's Thesis, Soonchunhyang Univ), 2022.URL
6 
K. H. Kim, H. S. Youn and Y. C. Kang, “Short-term Load Forecasting for Special Days in Anomalous Load Conditions Using Neural Networks and Fuzzy Inference Method,” IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 559-565, 2000.DOI
7 
K. B. Song, Y. S. Baek, D. H. Hong and G Jang, “Short-term Load Forecasting for the Holidays Using Fuzzy Linear Regression Method,” IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 96-101, 2005.DOI
8 
S. Ryu, M. Kim and H. Kim, “Denoising Autoencoder-based Missing Value Imputation for Smart Meters,” IEEE Access, vol. 8, pp. 40656-40666, 2020.DOI
9 
J. K. Kim, “Regression Fractional Hot Deck Impuration,” Journal of the Korean Statistical Society, vol. 36, no. 3, pp. 423-434, 2007.URL