JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Forecasting Daily Demand of Domestic City Gas with Selective Sampling
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Forecasting Daily Demand of Domestic City Gas with Selective Sampling
Lee, Geun-Cheol; Han, Jung-Hee;
  PDF(new window)
 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;
 Language
Korean
 Cited by
 References
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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)