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An approach of correlation inter-variable modeling with limited data for inter-bus transformer weather sensitive loading prediction
Fitrianaa, Hariyanto N.b
a Research Center for Electrical Engineering and Mechatronics, Indonesian Institute of Sciences, Indonesia
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung Gedung Kerma PLN-ITB Lt. 2, 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]This paper deals with a method to determine short-term loading estimation of inter-bus transformers based only a limited data of variables. A grey predicting approach is implemented in this paper to solve dynamic short term load forecasting affected by the climate changes in Indonesian EHV grid, as provided by Jawa Bali Control Center. A dynamic forecasting model is needed due to the uncertain nature of the load predicting process specifically when load changes is correlated with the external effect. Traditional GM (1,1) as basic model is presented to compare with the correlation inter-variable model in grey method for inter-bus loading transformers loading conditions refer to the temperature variation pattern during only the year 2009. GM(1,2) model denotes the correlation and relationship of hourly load demand affected to the 2 (two) tropical seasons, in this case by using local ambient temperature as dynamic variable. The daily load curve for Cawang and Cibatu distribution areas, as representing big local substations in Indonesia spatially, was used to validate the models. The model adequacy and weekly forecasting result had indicated a good and justified grade in error diagnostic checking (MAPE) in each location.[/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][/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]Ambient temperature records,Correlation inter-variable model,Grey dynamic forecasting model,Local load profiles[/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/10.15676/ijeei.2012.4.4.10[/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]