TY - GEN
T1 - Forecasting Electricity Consumption Based on Economics and Social Indicators Using Var Model with Exogenous Variable
T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
AU - Farih, Imaduddin
AU - Prastyo, Dedy Dwi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Economic conditions are very important to get attention. Economic growth is one indicator of community welfare in a region. Indonesia is an archipelagic country with 34 provinces, where each province has different social, cultural, and geographical conditions, so country-level economic conditions have not been able to fully describe the economic conditions in each region. This research was conducted to meet the need for a more specific analysis about the relationship between several regional economic indicators. One of the region's economic indicators is Gross Regional Domestic Income (GRDP). In addition, other factors are considered to affect economic indicators, such as the total population in the area and electrical consumption. It needs to be analyzed to obtain knowledge regarding the relationship between these variables. Time series data used as material analysis taken from 1991 to 2021. In this study, multivariate time series analysis was used with the Vector Autoregressive (VAR) model through several testing stages such as stationarity, cointegration, residual, causality, and others to obtain the form of optimal models. From the analysis process that has been carried out, the results show that the growth of electricity consumption influences the growth of GRDP but not vice versa (unidirectional causality). This shows that policies related to the availability of electrical energy and the use of electrical energy will be able to have a positive influence on GRDP growth. In this study, the forecasting process was also carried out based on the obtained model. So we have the prediction value for electricity consumption given the predicted GRDP available for the next few years, which can be used in planning strategies or policies for the Government and related parties.
AB - Economic conditions are very important to get attention. Economic growth is one indicator of community welfare in a region. Indonesia is an archipelagic country with 34 provinces, where each province has different social, cultural, and geographical conditions, so country-level economic conditions have not been able to fully describe the economic conditions in each region. This research was conducted to meet the need for a more specific analysis about the relationship between several regional economic indicators. One of the region's economic indicators is Gross Regional Domestic Income (GRDP). In addition, other factors are considered to affect economic indicators, such as the total population in the area and electrical consumption. It needs to be analyzed to obtain knowledge regarding the relationship between these variables. Time series data used as material analysis taken from 1991 to 2021. In this study, multivariate time series analysis was used with the Vector Autoregressive (VAR) model through several testing stages such as stationarity, cointegration, residual, causality, and others to obtain the form of optimal models. From the analysis process that has been carried out, the results show that the growth of electricity consumption influences the growth of GRDP but not vice versa (unidirectional causality). This shows that policies related to the availability of electrical energy and the use of electrical energy will be able to have a positive influence on GRDP growth. In this study, the forecasting process was also carried out based on the obtained model. So we have the prediction value for electricity consumption given the predicted GRDP available for the next few years, which can be used in planning strategies or policies for the Government and related parties.
KW - Macroeconomic
KW - Multivariate Time series
KW - Vector Autoregression
UR - http://www.scopus.com/inward/record.url?scp=85150452246&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE57756.2022.10057638
DO - 10.1109/ICITISEE57756.2022.10057638
M3 - Conference contribution
AN - SCOPUS:85150452246
T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
SP - 373
EP - 378
BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2022 through 14 December 2022
ER -