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The application of seasonal latent variable in forecasting electricity demand as an alternative method

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  • Sumer, Kutluk Kagan
  • Goktas, Ozlem
  • Hepsag, Aycan

Abstract

In this study, we used ARIMA, seasonal ARIMA (SARIMA) and alternatively the regression model with seasonal latent variable in forecasting electricity demand by using data that belongs to "Kayseri and Vicinity Electricity Joint-Stock Company" over the 1997:1-2005:12 periods. This study tries to examine the advantages of forecasting with ARIMA, SARIMA methods and with the model has seasonal latent variable to each other. The results support that ARIMA and SARIMA models are unsuccessful in forecasting electricity demand. The regression model with seasonal latent variable used in this study gives more successful results than ARIMA and SARIMA models because also this model can consider seasonal fluctuations and structural breaks.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Policy.

Volume (Year): 37 (2009)
Issue (Month): 4 (April)
Pages: 1317-1322

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Handle: RePEc:eee:enepol:v:37:y:2009:i:4:p:1317-1322

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Web page: http://www.elsevier.com/locate/enpol

Related research

Keywords: Autoregressive integrated moving average (ARIMA) model Seasonal autoregressive integrated moving average (SARIMA) model Seasonal latent variable;

References

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Cited by:
  1. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.

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