[vc_empty_space][vc_empty_space]
SARIMA and artificial neural network models for forecasting electricity consumption of a microgrid based educational building
Wasesa M.a, Tiara A.R.a, Afrianto M.A.a, Ramadhan F.I.a, Haq I.N.a, Pradipta J.a
a Institut Teknologi Bandung, School of Business and Management, 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]© 2020 IEEE.We develop Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) models for predicting one-month and one-day ahead electricity consumption of a microgrid based educational building. The prediction models can provide forecasts up to hourly accuracy. For this objective, we use more than two million records of electricity consumption data imported from the smart meter system of a six-floor microgrid based educational building. We use the Hyndman-Khandakar stepwise algorithm, which generates the (1, 0, 1)×(0, 1, 1)24 SARIMA prediction models. For the ANN prediction models, we use a thirty one-neurons input layer, a twenty-neurons hidden layer, and a single neuron output layer. The experiment results indicate that the ANN models produce more accurate and consistent predictions than the SARIMA models both in the one-month ahead and one-day ahead prediction contexts.[/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]ANN prediction,Artificial neural network models,Educational buildings,Electricity-consumption,Forecasting electricity,Prediction model,Seasonal autoregressive integrated moving averages,Stepwise algorithms[/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]ANN,Electricity Consumption,Microgrid,Predictive Analytics,SARIMA[/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]This research was supported by the Multidisciplinary Research Grant 2020 of Institut Teknologi Bandung and the Conference Grant 2020 of School of Business and Management, Institut Teknologi Bandung.[/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/IEEM45057.2020.9309943[/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]