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An ARCH model the electric power of extra high voltage (EHV) transmission substation forecasting in Cawang, Jakarta, Indonesia
Pasaribu U.S.a, Setiyowati S.a, Mukhaiyar U.a
a Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2014 IEEE.An ARCH forecasting model for electric power demand of an EHV is proposed in this work. The power demand in EHV substation, Jakarta, Indonesia which passed through Cawang substation is continuously changed in values either normally or drastically. These electric demands were measured every 30 minutes so that there are 48 different values of electric demand that pass through a substation for one day. To distribute the power to the customers, PT Perusahaan Listrik Negara (PLN) has to determine which power plant will be used with the most optimum route and minimum operations budget. For that purpose, an ARCH modeling was applied so that the forecast power demand could be estimated. This model is applied to unveil the heteroscedasticity character of residuals with inconstant variance. In this work, MATLAB software and NUMXL were used for parameters estimation of model identification. As a result, it turns out that convergent parameter values obtained when 28 data observations were used. Therefore, the forecasting of electric power was generated in order to forecast up to ten steps ahead. This forecasting of the electric power could provide extensive and valuable information of electric power to PLN so that electric route and related budget can be optimized.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Electric power demands,Extra high voltage,Forecasting modeling,Heteroscedasticity,Matlab- software,Model identification,Non-stationary time series,Parameters estimation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ARCH,EHV (Extra High Voltage),forecasting,heteroscedasticity,non-stationary time series model[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/INAGENTSYS.2014.7005725[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]