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

Sumer K. K., Goktas O., Hepsag A.

ENERGY POLICY, cilt.37, sa.4, ss.1317-1322, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 4
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.enpol.2008.11.014
  • Dergi Adı: ENERGY POLICY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.1317-1322
  • Anahtar Kelimeler: Autoregressive integrated moving average(ARIMA) model, Seasonal autoregressive integrated moving average (SARIMA) model, Seasonal latent variable, GENETIC ALGORITHM APPROACH, PRIMARY ENERGY DEMAND, NEURAL-NETWORKS, CONSUMPTION, TURKEY, TREND, FUEL
  • İstanbul Üniversitesi Adresli: Evet

Özet

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. (C) 2008 Elsevier Ltd. All rights reserved.

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.