The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ([1,2,7,16,18,35,46], 1, [1,3,13,21,27,46])(1,1,1)48(0,0,1)336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.

Original languageEnglish
Pages (from-to)4892-4901
Number of pages10
JournalInternational Journal of Electrical and Computer Engineering
Issue number6
Publication statusPublished - Dec 2018


  • DSARIMA model
  • Electrical power demand
  • Forecasting
  • Least squares method
  • Time-series pattern


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