The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Forecasts of electricity consumption in an industry building

광, 공업용 건물의 전기 사용량에 대한 시계열 분석

  • Kim, Minah (Department of Statistics, Duksung Women's University) ;
  • Kim, Jaehee (Department of Statistics, Duksung Women's University)
  • 김민아 (덕성여자대학교 정보통계학과) ;
  • 김재희 (덕성여자대학교 정보통계학과)
  • Received : 2017.12.21
  • Accepted : 2018.03.08
  • Published : 2018.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study is on forecasting the electricity consumption of an industrial manufacturing building called GGM from January 2014 to April 2017. We fitted models using SARIMA, SARIMA + GARCH, Holt-Winters method and ARIMA with Fourier transformation. We also forecasted electricity consumption for one month ahead and compared the predicted root mean square error as well as the predicted error rate of each model. The electricity consumption of GGM fluctuates weekly and annually; therefore, SARIMA + GARCH model considering both volatility and seasonality, shows the best fit and prediction.

본 연구는 2014년 1월부터 2017년 4월까지 광, 공업용 제조업을 하는 건물(GGM)의 전기 사용량에 대한 예측을 살펴보고자 한다. SARIMA, SARIMA + GARCH, Holt-Winters 방법, Fourier 변환으로 분해를 한 ARIMA 모형을 중심으로 네 가지 모형에 대한 적합을 하였다. 또한 2017년 5월 사용량에 대한 예측하고, 실제값을 고려하여 각 모형에 대해 예측 제곱근 평균 제곱 오차와 예측 오차율을 비교하였다. GGM 건물의 전기 사용량에 대한 변동이 심하기 때문에 여러 가지 모형 중에서도 변동성과 주기를 함께 고려한 SARIMA + GARCH 모형의 적합과 예측이 가장 뛰어난 것을 확인하였다.

Keywords

References

  1. Abdel-Aal, R. E, and Al-Garni, A. Z. (1997). Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis, Energy, 22, 1059-1069. https://doi.org/10.1016/S0360-5442(97)00032-7
  2. Ali, G. (2013). EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH models for pathogens at marine recreational sites, Journal of Statistical and Econometric Methods, 2, 57-73.
  3. Asante-Darko, D., Adabor, E. S., and Amponsah, S. K. (2016). A Fourier series model for forecasting solid waste generation in the Kumasi metropolis of Ghana, WIT Transactions on Ecology and the Environment, 202, 173-185.
  4. Bianchi, L., Jarrett, J., and Hanumara, R. C. (1998). Improving forecasting for telemarketing centers by ARIMA modeling with intervention, International Journal of Forecasting, 14, 497-504. https://doi.org/10.1016/S0169-2070(98)00037-5
  5. Billah, B., King, M. L., Snyder, R. D., and Koehler, A. B. (2006). Exponential smoothing model selection for forecasting, International journal of forecasting, 22, 239-247. https://doi.org/10.1016/j.ijforecast.2005.08.002
  6. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  7. Broock, W. A., Scheinkman, J. A., Dechert, W. D., and LeBaron, B. (1996). A test for independence based on the correlation dimension, Econometric Reviews, 15, 197-235. https://doi.org/10.1080/07474939608800353
  8. Choi, B., Kang, H., Lee, K. Y., and Han, S. T. (2009). A development of time-series model for City Gas demand forecasting, The Korean Journal of Applied Statistics, 22, 1019-1032. https://doi.org/10.5351/KJAS.2009.22.5.1019
  9. Chujai, P., Kerdprasop, N., and Kerdprasop, K. (2013). Time series analysis of household electric consumption with ARIMA and ARMA models, the International MultiConference of Engineers and Computer Scientists, 1, 295-300.
  10. Cryer, J. and Chan, K. (2008). Time Series Analysis 2nd, Springer, New York.
  11. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, 50, 987-1007. https://doi.org/10.2307/1912773
  12. Fumi, A., Pepe, A., Scarabotti, L., and Schiraldi, M. M. (2013). Fourier analysis for demand forecasting in a fashion company, International Journal of Engineering Business Management, 5, 30. https://doi.org/10.5772/56839
  13. Gardner, E. S. (1985). Exponential smoothing: The state of the art, Journal of forecasting, 4, 1-28. https://doi.org/10.1002/for.3980040103
  14. Gonzalez-Romera, E., Jaramillo-Moran, M. A., and Carmona-Fernandez, D. (2008). Monthly electric energy demand forecasting with neural networks and Fourier series, Energy Conversion and Management, 49, 3135-3142. https://doi.org/10.1016/j.enconman.2008.06.004
  15. Huang, Y.-F., Chen, P.-J., and Nguyen, T.-L. (2014) Forecasting with Fourier residual modified ARIMA model- an empirical case of inbound tourism demand in New Zealand, Recent Researches in Applied Economics and Management, 2, 61-65.
  16. Kim, B. and Kim, J. (2013). Time series models for daily exchange rate data, The Korean Journal of Applied Statistics, 26, 1-14. https://doi.org/10.5351/KJAS.2013.26.1.001
  17. Kimball, B. A. (1974). Smoothing data with Fourier transformations, Agronomy journal, 66, 259-262. https://doi.org/10.2134/agronj1974.00021962006600020023x
  18. Lye, K. W., Yuan, M., and Cai, T. X. (2009). A spectrum comparison method for demand forecasting, SIMTech technical reports, 10, 32-35.
  19. McLeod, A. I. and Li, W. K. (1983). Diagnostic checking ARMA time series models using squared residual autocorrelations, Journal of Time Series Analysis, 4, 269-273. https://doi.org/10.1111/j.1467-9892.1983.tb00373.x
  20. Tan, Z., Zhang, J., Wang, J., and Xu, J. (2010). Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models, Applied Energy, 87, 3606-3610. https://doi.org/10.1016/j.apenergy.2010.05.012
  21. Walker, J. S. (1991). Fourier Series, Oxford University Press, New York.