Communications for Statistical Applications and Methods

  • 제22권6호
  • /
  • Pages.675-683
  • /
  • 2015
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Comparison of Electricity Forecasting Models: Korea and China

  • Zheng, Xueyan (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2015.10.05
  • 심사 : 2015.11.16
  • 발행 : 2015.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

키워드

참고문헌

  1. Aizerman, A., Braverman, E. M. and Rozoner, L. I. (1964). Theoretical foundations of the potential function method in pattern recognition learning, Automation and Remote Control, 25, 821-837.
  2. Box, G. E. P. and Jenkins, G. M. (1970). Time series analysis, Forecasting and Control, Holden-Day, Oakland, CA.
  3. Claveria, O., Monte, E. and Torra, S. (2015). Regional Forecasting with Support Vector Regressions: The Case of Spain, University of Barcelona, Regional Quantitative Analysis Group.
  4. Fletcher, R. (1987). Practical Methods of Optimization, Wiley, New York, 1, 784-794.
  5. Karin, K. (2011). Forecasting electricity demand in Thailand with and artificial neural network approach, Journal of Energies, 4, 1246-1257. https://doi.org/10.3390/en4081246
  6. Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing, Journal of the Operational Research Society, 54, 799-805. https://doi.org/10.1057/palgrave.jors.2601589
  7. Taylor, J.W. (2010). Triple seasonal methods for short-term electricity demand forecasting, European Journal of Operational Research, 204, 139-152. https://doi.org/10.1016/j.ejor.2009.10.003
  8. Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer, New York.
  9. Wang, J., Zhu, W., Zhang, W. and Sun, D. (2009). A trend fixed on firstly and seasonal adjustment model combined with the ${\epsilon}$-SVR for short-term forecasting of electricity demand, Energy Policy, 37, 4901-4909. https://doi.org/10.1016/j.enpol.2009.06.046
  10. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages, Management Science, 6, 324-342. https://doi.org/10.1287/mnsc.6.3.324
  11. Yoon, S. H., Lee, Y. S. and Park, J. S. (2009). Statistical modeling for forecasting maximum electricity demand in Korea, Communications for Statistical Applications and Method, 16, 127-135. https://doi.org/10.5351/CKSS.2009.16.1.127

피인용 문헌

  1. An empirical study of Chinese consumers' lifestyle by country of origin effect of mobile phone vol.27, pp.6, 2016, https://doi.org/10.7465/jkdi.2016.27.6.1565