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References

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J. Lee and F. Zhao, Global Wind Report 2024, Global Wind Energy Council (GWEC), pp. 18, 2024.URL
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Ministry of Trade, Industry and Energy (MOTIE), The 10th Basic Plan of Long-Term Electricity Supply and Demand (2022~2036), pp. 51~88, 2023.URL
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N. Mlilo, J. Brown and T. Ahfock, “Impact of intermittent renewable energy generation penetration on the power system networks - A review,” Technology and Economics of Smart Grids and Sustainable Energy, vol. 6, no. 1, pp. 25, 2021. DOI:10.1007/s40866-021-00123-wDOI
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Y. Lee, H. Kim, D. Lee, C. Lee and D. Lee, “Validation of Forecasting Performance of Two-Stage Probabilistic Solar Irradiation and Solar Power Forecasting Algorithm using XGBoost,” The transactions of The Korean Institute of Electrical Engineers, vol. 68, no. 12, pp. 1704-1710, 2019. DOI:10.5370/KIEE.2019.68.12.1704DOI
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S. H. Choi and J. Hur, “Optimized-XGBoost Learner Based Bagging Model for Photovoltaic Power Forecasting,” The transactions of The Korean Institute of Electrical Engineers, vol. 69, no. 7, pp. 978-984, 2020. DOI:10.5370/KIEE.2020.69.7.978DOI
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Y. Choy, J. Baek, D. H. Jeon, S. H. Park, S. Choi, Y. Kim and J. Hur, “Development of Prediction Model for Renewable Energy Environmental Variables Based on Kriging Techniques,” KEPCO Journal on Electric Power and Energy, vol. 5, no. 3, pp. 223-228, 2019. DOI:10.18770/KEPCO.2019.05.03.223.DOI
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J. Oh, D. H. Ham, Y. Lee and G. Kim, “Short-term Load Forecasting Using XGBoost and the Analysis of Hyperparameters,” The transactions of The Korean Institute of Electrical Engineers, vol. 68, no. 9, pp. 1073-1078, 2019. DOI:10.5370/KIEE.2019.68.9.1073DOI
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S. K. Lee and S. J. Yoo, “Predicting Real Estate Fractional Investment Prices with the XGBOOST Model : Centered on the Kasa TE Logistics Center,” GRI REVIEW, vol. 26, no. 1, pp.1-22, 2024. DOI:10.23286/gri.2024.26.1.001DOI