Medium-term Electricity Load Demand Forecasting using Holt-Winter Exponential Smoothing and SARIMA in University Campus
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Rosnalini Mansor, Bahtiar Jamili Zaini, Catherine Chan May May
|DOI : 10.14445/22315381/IJETT-V69I3P225|
MLA Style: Rosnalini Mansor, Bahtiar Jamili Zaini, Catherine Chan May May "Medium-term Electricity Load Demand Forecasting using Holt-Winter Exponential Smoothing and SARIMA in University Campus" International Journal of Engineering Trends and Technology 69.3(2021):165-171.
APA Style:Rosnalini Mansor, Bahtiar Jamili Zaini, Catherine Chan May May. Medium-term Electricity Load Demand Forecasting using Holt-Winter Exponential Smoothing and SARIMA in University Campus International Journal of Engineering Trends and Technology, 69(3),165-171.
This study was conducted to determine the best model in forecasting the electricity load demand for the next 1, 2, and 3 months in a university in Malaysia by comparing 4 error measures, such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Percentage Error, and Geometric Root Mean Squared Error. Two forecasting methods were compared in this study to obtain the best forecasting results. The methods are Holt-Winter’s Exponential Smoothing (HWES) method and Seasonal Autoregressive Integrated Moving Average (SARIMA). This study used 68 secondary data from January 2010 until August 2015. Microsoft Excel and JMP were used to determine the type of time series components, model estimation, and model evaluation. The results showed that seasonal and trend components exist in the dataset. The best model was SARIMA (0,0,1) (1,0,0), 12 models since it denoted the lowest error measurement compared to HWES. Then, this model is used for forecasting the next 1, 2, and 3 months in load demand of electricity.
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Electricity load demand, Holt-Winter exponential smoothing, Medium-term forecasting, Seasonal autoregressive integrated moving average