TY - JOUR
T1 - Applying of double seasonal arima model for electrical power demand forecasting at pt. pln gresik Indonesia
AU - Mado, Ismit
AU - Soeprijanto, Adi
AU - Suhartono,
N1 - Publisher Copyright:
Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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.
KW - DSARIMA model
KW - Electrical power demand
KW - Forecasting
KW - Least squares method
KW - Time-series pattern
UR - http://www.scopus.com/inward/record.url?scp=85057425488&partnerID=8YFLogxK
U2 - 10.11591/ijece.v8i6.pp4892-4901
DO - 10.11591/ijece.v8i6.pp4892-4901
M3 - Article
AN - SCOPUS:85057425488
SN - 2088-8708
VL - 8
SP - 4892
EP - 4901
JO - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
IS - 6
ER -