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http://dx.doi.org/10.5762/KAIS.2015.16.10.6860

Forecasting Daily Demand of Domestic City Gas with Selective Sampling  

Lee, Geun-Cheol (College of Business Administration, Konkuk University)
Han, Jung-Hee (Department of Business Administration, Kangwon National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.16, no.10, 2015 , pp. 6860-6868 More about this Journal
Abstract
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.
Keywords
City gas; Daily demand; Forecasting; Regression; Selective Sampling;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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1 Y. S. Chang, B. H. Kang, "Survey Analysis on Domestic Utilization of Natural Gas", The 2013 Spring Conference Proceedings of the Society of Air-conditioning and Refrigerating Engineers of Korea, 352-355, 2013.
2 J.-J. Her, H.-J. Lim, "An Analysis of Growth Factors on the City-gas Industry by Input-output Structural Decomposition Analysis", Journal of Energy Engineering, 21(2), 158-167, 2012. DOI: http://dx.doi.org/10.5855/ENERGY.2012.21.2.158   DOI
3 KEEI Quarterly Energy Outlook, Korea Energy Economics Institute, 16(3), 2014.
4 KESIS, http://www.kesis.net/ (accessed July 2015)
5 S.-J. Lee, S.-S. Euh, S.-H. Yoo, "Estimation of City Gas Demand Function Using Time Series Data", Journal of Energy Engineering, 22(4), 370-375, 2013. DOI: http://dx.doi.org/10.5855/ENERGY.2013.22.4.370   DOI
6 H.-Y. Oh, "Forecasting of the Short-Term Demand for the Natural Gas Using Time Series Analysis and Artificial Neural Networks", Master Thesis, Graduate School of Management, KAIST, 1997.
7 J.-S. Kim, C.-S. Yang, J.-G. Park, "An Empirical Study on the Consumption Function of Korean Natural Gas for City Gas", Journal of Energy Engineering, 20(4), 318-329, 2011. DOI: http://dx.doi.org/10.5855/ENERGY.2011.20.4.318   DOI
8 B. Choi, H. Kang, K.-Y. Lee, S. T. Han, "A Development of Time-series Model for City Gas Demand Forecasting", Korean Journal of Applied Statistics, 22(5), 1019-1032, 2009. DOI: http://dx.doi.org/10.5351/KJAS.2009.22.5.1019   DOI
9 J. S. Park, Y. B. Kim, C. W. Jung, "Short-Term Forecasting of City Gas Daily Demand", Journal of the Korean Institute of Industrial Engineers, 39(4), 247-252, 2013. DOI: http://dx.doi.org/10.7232/JKIIE.2013.39.4.247   DOI
10 C. W. Jung, "A Study on City Gas Demand Forecasting Based on Daily Characteristics", Master Thesis, Department of Industrial Engineering, Sungkyunkwan University, 2013.
11 B. Soldo, "Forecasting Natural Gas Consumption", Applied Energy, 92, 26-37, 2012. DOI: http://dx.doi.org/10.1016/j.apenergy.2011.11.003   DOI
12 A. Azadeh, S.M. Asadzadeh, A. Ghanbari, "An Adaptive Network-based Fuzzy Inference System for Short-term Natural Gas Demand Estimation: Uncertain and Complex Environments", Energy Policy, 38, 1529-1536, 2010. DOI: http://dx.doi.org/10.1016/j.enpol.2009.11.036   DOI
13 F. Taspinar, N. Celebi, N. Tutkun, "Forecasting of Daily Natural Gas Consumption on Regional Basis in Turkey Using Various Computational Methods", Energy and Building, 56, 23-31, 2013. DOI: http://dx.doi.org/10.1016/j.enbuild.2012.10.023   DOI
14 L. Zhu, M. S. Li, Q. H. Wu, L. Jiang, "Short-term Natural Gas Demand Prediction Based on Support Vector Regression with False Neighbors Filtered", Energy, 80, 428-436, 2015. DOI: http://dx.doi.org/10.1016/j.energy.2014.11.083   DOI
15 O.-S. Kwon, K.-B. Song, Development of Short-Term Load Forecasting Method by Analysis of Load Characteristics during Chuseok Holiday", The Transactions of The Korean Institute of Electrical Engineers, 60(12), 2215-2220, 2011. DOI: http://dx.doi.org/10.5370/KIEE.2011.60.12.2215   DOI
16 K.-B. Song, J.-H. Lim, "Short-Term Load Forecasting for the Consecutive Holidays Considering Businesses' Operation Rates of Industries", The Transactions of The Korean Institute of Electrical Engineers, 62(12), 1657-1660, 2013. DOI: http://dx.doi.org/10.5370/KIEE.2013.62.12.1657   DOI