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Forecasting Daily Demand of Domestic City Gas with Selective Sampling
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 Title & Authors
Forecasting Daily Demand of Domestic City Gas with Selective Sampling
Lee, Geun-Cheol; Han, Jung-Hee;
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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.
City gas;Daily demand;Forecasting;Regression;Selective Sampling;
 Cited by
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