Forecasting of Electricity Consumption and Supply for Campus University using Time Series Models

Abstract

Electricity is an important energy source in university as lecture classes need electricity supply to function. It is also important for the development of the university. Since electricity consumption is a necessity of a university’s operation, the forecast of electricity consumption on the university campus should be made. This is essential for the development of the university as the treasury department can manage the funding from the government according to the value forecasted to make full use of the funding in the university’s development. There are several forecasting methods used in this study, including time series regression, seasonal exponential smoothing, Box-Jenkins (SARIMA), decomposition and the naïve method. Error measurements used to evaluate the performance of forecasting model were mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and geometric root mean square error (GRMSE). The results of this study showed that the seasonal exponential smoothing model was the best in the 1-step ahead and 2-step ahead forecasting while SARIMA (0,2,2)(0,2,1)₁₂ was the best in the 3-step ahead forecast. The overall performance of seasonal exponential smoothing was the best in this study. Throughout this study, suggestions were made for the next study regarding electricity consumption in university to consider factors such as semester breaks and students’ activities in order to examine its effect in electricity consumption.