ELECTRICITY DEMAND FORECASTING IN MALAYSIA USING SEASONAL BOX-JENKINS MODEL

The development of a precise forecasting model for electricity demand is essential for optimizing the efficiency of planning within the power generation sector. The electricity demand data in Malaysia exhibits seasonal patterns, making it necessary to evaluate the forecasting capabilities of the Box-Jenkins model for predicting weekly peak electricity demand. The objective of this study is to assess how well the Box-Jenkins model performs in forecasting the weekly peak electricity demand. This study utilizes weekly electricity demand data, specifically the highest values recorded each week, measured in megawatts (MW), spanning from 2005 to 2016. The findings indicate that SARIMA (4,1,0)(0,1,0)52 is the best-suited choice for predicting electricity demand. This conclusion is supported by its notably low values of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) which stand at 623.3015, 488.5673, and 2.95%, respectively. The MAPE value of the suggested model, falling below the 5% threshold, suggests that the seasonal Box-Jenkins model performs quite effectively when it comes to predicting electricity demand in the context of Malaysian data. To summarize, the proposed seasonal Box-Jenkins model exhibits significant potential and delivers promising performance when forecasting electricity demand characterized by seasonal patterns.