The growth of the human population and technology has led to a rapid increase in electrical energy consumption. Excess electrical energy would be detrimental to the provider, whereas providing less would be detrimental to the consumers. One method for reducing these losses is to forecast the amount of electrical energy that must be available to meet demand. Prediction results can help with three types of decisions, depending on the prediction period: operational decisions (short-term), tactical decisions (medium-term), and strategic decisions (long-term). Short-term forecasts are less relevant given the urgency of the situation. This study aims to help electricity providers to make decisions by making medium and long-term predictions using the Auto-Regressive Integrated Moving Average (ARIMA) method. In the best order determination experiment, ARIMA (8,2,0) was found to be the best model with the smallest error. ARIMA (8,2,0) has an average percentage error of 5.3 percent based on the overall prediction results. There is no linearity between accuracy and prediction period in the prediction period experiment. According to the experimental results, the highest accuracy is obtained in the medium term (monthly) with a value of RMSE 753,983.98. As a result, based on the time period, ARIMA is the best for tactical decisions (medium-term) regarding electrical energy consumption.