TELKOMNIKA Telecommunication, Computing, Electronics and Control
Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model
Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model
Subject
Akaike information criterion
Autoregressive integrated
moving average
Energy consumption
Holt Winters
Management of energy
Time series forecasting
Autoregressive integrated
moving average
Energy consumption
Holt Winters
Management of energy
Time series forecasting
Description
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.
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.
Creator
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
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Jun 17, 2020
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
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 Telecommunication, Computing, Electronics and Control
Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/3796.
Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/3796.