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2022 Vol.42, Issue 6 Preview Page

Research Article

30 December 2022. pp. 173-183
Abstract
References
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KPX, 20222. https://new.kpx.or.kr/board.es?mid=a10109010700&bid=0082&act=view&list_no=68145 last accessed on the 14th December 2022.
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Ministry of Commerce, Industry and Energy, Introduction of Renewable Energy Generation Prediction System, 2020.
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Lee, J., Park, W., Lee, I., and Kim, S., Comparison of Solar Power Prediction Model Based on Statistical and Artificial Intelligence Model and Analysis of Revenue for Forecasting Policy, Journal of IKEEE, Vol. 26, No. 3, pp. 355-363, 2022.
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Sohn, H., Jung, S., and Kim, S., The Prediction and Valuation of Gas Consumption in Building using Artificial Neural Networks Based on Clustering Method, The Korean Journal of Applied Statistics, Vol. 29, No. 1, pp. 193-203, 2016. 10.5351/KJAS.2016.29.1.193
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Choi, D., Lee, Y., and Ko, M., A Study on Electricity Demand Forecasting Based on Time Series Clustering in Smart Grid, Korea Institute of Ecological Architecture and Environment, Vol. 18, No. 5, pp. 69-74, 2018.
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Ke, G, Meng, Q., Finely, T., Wang, T., Chen, W., and Ma, W., Lightgbm: A Highly Efficient Gradient Boosting Decision Tree, Advances in Neural Information Processing System, Vol. 30, pp. 3146-3154, 2017.
Information
  • Publisher :Korean Solar Energy Society
  • Publisher(Ko) :한국태양에너지학회
  • Journal Title :Journal of the Korean Solar Energy Society
  • Journal Title(Ko) :한국태양에너지학회 논문집
  • Volume : 42
  • No :6
  • Pages :173-183
  • Received Date :2022. 11. 07
  • Revised Date :2022. 12. 16
  • Accepted Date : 2022. 12. 21