[vc_empty_space][vc_empty_space]
Medium Term Power Load Forecasting for Java and Bali Power System Using Artificial Neural Network and SARIMAX
Hutama A.H.a, Akbar S.a, Catur Candra M.Z.a
a Institut Teknologi Bandung, Sekolah Teknik Elektro Dan Informatika, Bandung, 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]© 2018 IEEE.Power load forecasting is an important part of electrical company operations. An accurate forecast can help the company makes various important decisions. Two known models for power load forecasting are ARMA model and its variants and Artificial Neural Network (ANN). The ARMA model has been used for decades while ANN can be considered as a more recent approach. In this paper MLP and SARIMAX are proposed to model the power load of Java and Bali power system. Both models can be used to forecast the load of Java and Bali power system with MAPE of 2.4% for SARIMAX and 2.7% for MLP. The time needed to build a SARIMAX model is shorter compared to MLP. In general, SARIMAX performs better compared to MLP. An application is also developed to facilitate data transformation, model training, and forecasting based on the proposed models.[/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]ARMA model,Company operation,Data transformation,Exogenous variables,Mean absolute percentage error,Medium term,Model training,Power load forecasting[/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]Artificial neural network (ANN),Mean absolute percentage error (MAPE),Power load forecasting,Seasonal autoregressive integrated moving average with exogenous variable (SARIMAX)[/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/ICODSE.2018.8705837[/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]