Forecasting Electricity Consumption Using Time Series Model
Keywords:Centered Moving Average, Holt Linear Trend, Holt-Winters, MAE, MAPE, MSE, RMSE, Simple Exponential Smoothing, Simple Moving Average, Weighted Moving Average
Electricity demand forecasting is important for planning and facility expansion in the electricity sector. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. Universiti Tun Hussein Onn Malaysia (UTHM) which is a developing university in Malaysia has been growing since its formation in 1993. Thus, it is important for UTHM to forecast the electricity consumption in future so that the future development can be determined. Hence, UTHM electricity consumption was forecasted by using the simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW) and centered moving average (CMA). The monthly electricity consumption from January 2011 to December 2017 was used to forecast January to December 2018 monthly electricity consumption. HW gives the smallest mean absolute error (MAE) and mean absolute percentage error (MAPE), while CMA produces the lowest mean square error (MSE) and root mean square error (RMSE). As there is a decreasing population of UTHM after the moving of four faculties to Pagoh and HW forecasted trend is decreasing whereas CMA is increasing, hence HW might forecast better in this problem.
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