Main Article Content
Forecasting electric consumption if accurate can be useful information to the administrators and those concerned to set the policy for saving the electric consumption. As a consequence, the cost of electric consumption could be reduced. The research aimed to forecast the electric consumption of Ubon Ratchathani Rajabhat University. Three methods were employed: Holt-Winters Method, SARIMA and combined method. Data used in the work were the time series of a monthly electric consumption. Data were collected from the Ministry of Energy from January 2005 to 2017 totaling 149 value sets. Data were categorized into two sets: one from January 2005 to December 2016 (144 value sets) and the other from January to December, 2017. Data gained in set one were used as the forecasting model by using R language. Then, data gained in set two were used to compare the accuracy of the three methods. Based on the experiment, it was found that SARIMA was the one with the lowest Mean Absolute Percentage Error (MAPE). Therefore, SARIMA was the most suitable model to be used to forecast the electric consumption of Ubon Ratchathani Rajabhat University.
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