발전용 천연가스 일일수요 예측 모형 연구-평일수요를 중심으로

  • 정희엽 (고려대학교 그린스쿨, 한국가스공사) ;
  • 박호정 (고려대학교 그린스쿨)
  • 발행 : 2018.08.31

초록

Natural gas demand for power generation continued to increase until 2013 due to the expansion of large-scale LNG power plants after the black-out of 2011. However, natural gas demand for power generation has decreased sharply due to the increase of nuclear power and coal power generation. But demand for power generation has increased again as energy policies have changed, such as reducing nuclear power and coal power plants, and abnormal high temperatures and cold waves have occurred. If the gas pipeline pressure can be properly maintained by predicting these fluctuations, it can contribute to enhancement of operation efficiency by minimizing the operation time of facilities required for production and supply. In this study, we have developed a regression model with daily power demand and base power generation capacity as explanatory variables considering characteristics by day of week. The model was constructed using data from January 2013 to December 2016, and it was confirmed that the error rate was 4.12% and the error rate in the 90th percentile was below 8.85%.

키워드

참고문헌

  1. A. Goncu, M. Karahan, T. Kuzubas. 2013. Forecasting Daily Residential Natural Gas Consumption: A Dynamic Temperature Modelling Approach. Available at https://www.openaire.eu.
  2. Akpinar. M, Adak. F, Yumusak. N. 2017. Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey. Energies 10(6): 1-20.
  3. Bae YJ, Chung JW. 2017. Forecasting demand variability of Liquefied Natural Gas: focused on household demand. Journal of Business Research 32(3): 239-259. https://doi.org/10.1016/0148-2963(94)00049-K
  4. Choi BS, Kang HC, Lee KY, Han ST. 2009. A Development of Time-Series Model for City Gas Demand Forecasting. The Korean journal of applied statistics 22(5): 1019-1032. https://doi.org/10.5351/KJAS.2009.22.5.1019
  5. GC Lee, JH Han. 2015. Forecasting Daily Demand of Domestic City Gas with Selective Sampling. Journal of the Korea Academia-Industrial copoeration Society 16(10): 6860-6868 (in Korean with English abstract). https://doi.org/10.5762/KAIS.2015.16.10.6860
  6. Greenhouse Gas Inventory and Research Center. 2017. 2017 National Greenhouse Gas Inventory Report of Korea. Available at http://www.gir.go.kr.
  7. Han JH, Baek JK. 2011. Forecasting Daily Demand of Electric Power in Summer Using a Regression Model. Journal of Commodity Science and Technology 29(50): 69-75.
  8. JS Park, YB Kim, CW Jung. 2013. Short-Term Forecasting of City Gas Daily Demand. Journal of the Korean Institute of Industrial Engineers 39(4): 247-252 (in Korean with English abstract). https://doi.org/10.7232/JKIIE.2013.39.4.247
  9. JS Kim, CS Yang, JG Park. 2011. An Empirical Study on the Consumption Function of Korean Natural Gas for City Gas: Using Time Varying Coefficient Time Series Model. Journal of Energy Engineering 20(4): 318-329 (in Korean with English abstract). https://doi.org/10.5855/ENERGY.2011.20.4.318
  10. KESIS. 2018. Monthly natural gas production and consumption. Availble at http://www.kesis.net.
  11. Khotanzad. A., Elragal. H., 1999. Natural gas load forecasting with combination of adaptive neural networks. Proceedings of the International Joint Conference on Neural Networks 6: 4069-4072.
  12. Khotanzad. A., Elragal. H., Lu. T.-L., 2000. Combination of artificial neural-network forecasters for prediction of natural gas consumption. IEEE Transactions on Neural Networks 11(2): 464-473. https://doi.org/10.1109/72.839015
  13. KOGAS. 2018. production and supply of Main Business. Available at http://www.kogas.or.kr
  14. Korea Power Exchange. 2018. 2017 Summary of Electric Power Market Statistics. Available at http://epsis.kpx.or.kr
  15. Park CW, 2009. A Study of Short-run Demand Forecasts for Natural Gas. Dissertation. Sungkyunkwan university.
  16. Soldo. B. 2012. Forecasting natural gas consumption. Applied Energy 92: 26-37 https://doi.org/10.1016/j.apenergy.2011.11.003
  17. YD Kim, IG Na. 2002. Temperature effect on Natural gas Demand in Korea. Korean Association of Applied Economics. 4(2): 51-78.