The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Nonlinear impact of temperature change on electricity demand: estimation and prediction using partial linear model

기온변화가 전력수요에 미치는 비선형적 영향: 부분선형모형을 이용한 추정과 예측

  • Park, Jiwon (Korea Institute of Industrial Economics and Trade) ;
  • Seo, Byeongseon (Department of Food and Resource Economics, Korea University)
  • Received : 2019.06.26
  • Accepted : 2019.09.02
  • Published : 2019.10.31
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Abstract

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

최근 빈번하게 발생하는 이상기온과 기후변화로 인하여 전력수요의 변동성이 커지고 있으며 기온 영향의 증가와 함께 기온변화에 대한 전력수요의 반응은 비선형성과 비대칭성으로 나타나고 있다. 정부 에너지 정책의 변화와 4차 산업혁명의 전개에 따라 기온 효과를 보다 정확하게 추정하고 예측하는 것은 안정적 전력수급 관리를 위하여 중요한 과제이다. 본 연구는 기온변화에 대한 전력수요의 비선형적 반응에 대하여 부분선형모형을 이용하여 분석하고자 한다. 기온변화와 전력수요의 비선형·비대칭적 관계를 측정하기 위하여 Robinson의 double residual 준모수적 추정과 스플라인 추정을 적용하였다. 기상변수와 전력 소비에 대한 시간 단위 고주기 자료를 사용하여 부분선형모형으로 추정한 기온변화와 전력 소비의 관계는 기존 모수적 모형과는 다른 비선형성과 비대칭성을 갖고 있음을 확인하였다. 부분선형모형을 이용하여 얻은 전력수요에 대한 표본내·표본외 예측은 이차함수 모형과 냉난방도일 모형과 비교하여 우수한 예측력을 보였다. Diebold-Mariano 검정결과, 부분선형모형에서 얻은 예측력 향상은 통계적으로 유의하였다.

Keywords

References

  1. Bessec, M. and Fouquau, J. (2008). The non-linear link between electricity consumption and temperature in Europe: a threshold panel approach, Energy Economics, 30, 2705-2721. https://doi.org/10.1016/j.eneco.2008.02.003
  2. Diebold, F. and Mariano, R. (1995). Comparing predictive accuracy, Journal of Business and Economic Statistics, 13, 253-263. https://doi.org/10.2307/1392185
  3. Eilers, P. H. and Marx, B. D. (1996). Flexible smoothing with B-splines and penalties, Statistical Science, 11, 89-102. https://doi.org/10.1214/ss/1038425655
  4. Engle, R. F., Granger, C. W. J., Rice, J., and Weiss, A. (1986). Semiparametric estimates of the relation between weather and electricity sales, Journal of the American Statistical Association, 81, 310-320. https://doi.org/10.1080/01621459.1986.10478274
  5. Fan, Y. and Li, Q. (1999). Root-n-consistent estimation of partially linear time series models, Journal of Nonparametric Statistics, 11, 251-269. https://doi.org/10.1080/10485259908832783
  6. Hardle, W. and Mammen, E. (1993). Comparing nonparametric versus parametric regression fits, Annals of Statistics, 21, 1926-1947. https://doi.org/10.1214/aos/1176349403
  7. Moral-Carcedo, J. and Vicens-Otero, J. (2005). Modelling the Non-linear Response of Spanish Electricity Demand to Temperature Variations, Energy Economics, 27(3), 477-494. https://doi.org/10.1016/j.eneco.2005.01.003
  8. Office of Meteorology (2017). Report on Extreme Weather in 2016.
  9. Robinson, P. (1988). Root-n-consistent Semiparametric Regression, Econometrica, 56, 931-954. https://doi.org/10.2307/1912705
  10. Ruppert, D., Wand, M. P. and Carroll, R. J. (2003). Semiparametric Regression, Cambridge University Press.
  11. Shin, D. and Cho, H. (2014). Empirical study on climate sensitivity and threshold temperature of daily peak electricity demand in Korea, Economic Research, 32, 175-212.
  12. Speckman, P. (1988). Kernel Smoothing in Partial Linear Models, Journal of the Royal Statistical Society, Series B (Methodological), 50(3), 413-436. https://doi.org/10.1111/j.2517-6161.1988.tb01738.x
  13. Yatchew, A. (2003). Semiparametric Regression for the Applied Econometrician, Cambridge University.
  14. Yu, B. and Hwang, Y. (1997). Research on Forecasting the Peak Electricity Demand, Korea Energy Economics Institute, 1-56.