Forecasting Daily Demand of Domestic City Gas with Selective Sampling

선별적 샘플링을 이용한 국내 도시가스 일별 수요예측 절차 개발

  • Lee, Geun-Cheol (College of Business Administration, Konkuk University) ;
  • Han, Jung-Hee (Department of Business Administration, Kangwon National University)
  • Received : 2015.07.22
  • Accepted : 2015.10.08
  • Published : 2015.10.31


In this study, we consider a problem of forecasting daily city gas demand of Korea. Forecasting daily gas demand is a daily routine for gas provider, and gas demand needs to be forecasted accurately in order to guarantee secure gas supply. In this study, we analyze the time series of city gas demand in several ways. Data analysis shows that primary factors affecting the city gas demand include the demand of previous day, temperature, day of week, and so on. Incorporating these factors, we developed a multiple linear regression model. Also, we devised a sampling procedure that selectively collects the past data considering the characteristics of the city gas demand. Test results on real data exhibit that the MAPE (Mean Absolute Percentage Error) obtained by the proposed method is about 2.22%, which amounts to 7% of the relative improvement ratio when compared with the existing method in the literature.


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