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Journal from gdlhub / 2021-09-07 14:38:44
Oleh : Nahid Ferdous Aurna, Md. Tanjil Mostafa Rubel, Tanveer Ahmed Siddiqui, Tajbia Karim, Sabrina Saika, Md. Murshedul Arifeen, Tasmima Noushiba Mahbub, S. M. Salim Reza, Habibul Kabir, Telkomnika
Dibuat : 2021-09-07, dengan 0 file

Keyword : akaike information criterion, autoregressive integrated moving average, energy consumption, holt Winters, management of energy, time series forecasting
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/15303
Sumber pengambilan dokumen : Web

With the increasing demand of energy, the energy production is not that much sufficient and that’s why it has become an important issue to make accurate prediction of energy consumption for efficient management of energy. Hence appropriate demand side forecasting has a great economical worth. Objective of our paper is to render representations of a suitable time series forecasting model using autoregressive integrated moving average (ARIMA) and Holt Winters model for the energy consumption of Ohio/Kentucky and also predict the accuracy considering different periods (daily, weekly, monthly). We apply these two models and observe that Holt Winters model outperforms ARIMA model in each (daily, weekly and monthly observations) of the cases. We also make a comparison among few other existing analyses of time series forecasting and find out that the mean absolute percentage error (MASE) of Holt Winters model is least considering the monthly data.

Deskripsi Alternatif :

With the increasing demand of energy, the energy production is not that much sufficient and that’s why it has become an important issue to make accurate prediction of energy consumption for efficient management of energy. Hence appropriate demand side forecasting has a great economical worth. Objective of our paper is to render representations of a suitable time series forecasting model using autoregressive integrated moving average (ARIMA) and Holt Winters model for the energy consumption of Ohio/Kentucky and also predict the accuracy considering different periods (daily, weekly, monthly). We apply these two models and observe that Holt Winters model outperforms ARIMA model in each (daily, weekly and monthly observations) of the cases. We also make a comparison among few other existing analyses of time series forecasting and find out that the mean absolute percentage error (MASE) of Holt Winters model is least considering the monthly data.

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