The percentage of electrical energy needs is the largest demand compared to other energy needs such as natural gas, fuel, and coal. This is due to various factors, including population growth, economic growth, industrial development, as well as the rapid development of electricity-based technology in almost every sector, especially in the household, industrial and commercial sectors. This condition has the potential to trigger an electricity crisis, but can be minimized if the required electricity consumption is known. One way to determine electricity demand is to build a predictive model that is accurate and flexible and able to accommodate the complexity of seasonal patterns, both seasonal in months and years as reflected in electricity consumption data. Therefore, in this study, the TBATS model was used to accommodate this. TBATS models will be used which is the development of the exponential smoothing model that can accommodate the occurrence of multiple seasonal patterns, both nested and non-nested, non-integer seasonal periods, and handle the possibility of non-linearity cases because it has a flexible seasonality The results of this study, the TBATS model built has a value of SMAPE by 8.13% has been able to capture fluctuating patterns in seasonal periods.