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2020 Vol.57, Issue 6 Preview Page

Research Paper

December 2020. pp. 585-592
Abstract
References
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Jeong, S.Y., 2006. Day Ahead System Marginal Price Forecasting Using an Artificial Neural Network, MS Thesis, Kon Kuk University, Seoul, Korea, 41p.
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Jeong, S.Y., Lee, J.K., Park, J.B., Shin, J.R., and K, S.S., 2005. A hybrid neural network framework for hour-ahead system marginal price forecasting. The Transactions of the Korean Institute of Electrical Engineers (KIEE) Autumn Conference 2005, p.162-164.
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Kim, D.Y., Lee, C.J., Jeong, Y.W., Park, J.B., and Shin, J.R., 2006. Development of system marginal price forecasting method using ARIMA model. The Transactions of the Korean Institute of Electrical Engineers,55(2), p.85-93.
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Kim, D.Y., Lee, C.J., Lee, M.H., Park, J.B., and Shin, J.R., 2005. A day-ahead system marginal price forecasting using ARIMA model. The Transactions of the Korean Institute of Electrical Engineers (KIEE) Summer Conference 2005,p.819-821.
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Kim, N.Y., 2019. A Study on Power Price Prediction Model Based on Artificial Neural Networks, MS Thesis, Korea Polytechnic University, Seoul, Korea, 48p.
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Korea Energy Economics Institute, 2003. A Study on the Behavior of Market Participants in the Power Wholesale Market: Game Theoretical Approach, KEEI Report 2003-03, Ulsan, Korea, 91p.
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Korea Energy Economics Institute, 2013. A Study on the Stabilization of Power Market Price, KEEI Issue Paper 13-03, Ulsan, Korea, 23p.
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Korea Institute Industrial Economics & Trade, 2007. Major Issues and Problems of the Current Power Market Operating System and Direction of Improvement, KIET Industrial Economic Review Report 2007-07, Sejong, Korea, p.53-68.
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Lee, J.K., Park, J.B., Shin, J.R., and Lee, K.Y., 2005. A system marginal price forecasting method based on an artificial neural network using time and day information. International Federation of Automatic Control(IFAC) Journal 16th Triennial World Congress, Prague, Czech Republic, p. 122-127. 10.3182/20050703-6-CZ-1902.02256
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Lee, S.H., 2016. Forecasting volatility of weekday system marginal prices: a multi-frequency approach. Korean Energy Economic Review, 15(2), p.89-119.
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Shin, D., Baek, S., Lee, Y., and Kang, S., 2018. System marginal price time series data forecasting model development. The Korean Society of Mechanical Engineers,Kangwon Land Convention Center, Jeongseon-gun, Gangwon-do, p.3172-3176.
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Shin, D.Y. and Kim, J.H., 2015. The identification of structural shocks and analysis of system marginal price volatility in Korean electricity power market. Korean Association of Applied Economics, 17(2), p.121-166.
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Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 57
  • No :6
  • Pages :585-592
  • Received Date :2020. 11. 02
  • Revised Date :2020. 12. 04
  • Accepted Date : 2020. 12. 22