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http://dx.doi.org/10.5351/KJAS.2009.22.5.1019

A Development of Time-Series Model for City Gas Demand Forecasting  

Choi, Bo-Seung (Institute of Economics, Korea University)
Kang, Hyun-Cheol (Department Informational Statistics, Hoseo University)
Lee, Kyung-Yun (Department Business Management, Korea University)
Han, Sang-Tae (Department Informational Statistics, Hoseo University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.5, 2009 , pp. 1019-1032 More about this Journal
Abstract
The city gas demand data has strong seasonality. Thus, the seasonality factor is the majority for the development of forecasting model for city gas supply amounts. Also, real city gas demand amounts can be affected by other factors; weekday effect, holiday effect, the number of validity day, and the number of consumptions. We examined the degree of effective power of these factors for the city gas demand and proposed a time-series model for efficient forecasting of city gas supply. We utilize the liner regression model with autoregressive regression errors and we have excellent forecasting results using real data.
Keywords
City gas; demand forecast; regression with autoregressive errors; validity day effect; elasticity of temperature;
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