Performance Comparison of ARIMA, LSTM and SVM Models for Electric Energy Consumption Analysis

Abstract

The changing needs of electrical energy result in the electrical power needed for everyday life being unstable, so planning and predicting how much electrical load is needed so that the electricity generated is always of good quality. So it is necessary to predict the consumption of electrical energy by using forecasting on the machine learning method. Support Vector Machine (SVM), Autoregressive Integrated Motion Average (ARIMA), and Long Short-Term Memory (LSTM) are models that are often used to overcome patterns in predictions. To find out the best models how to predict electricity consumption in the future and how the SVM, LSTM, and ARIMA algorithms perform in predicting electricity consumption. This research will look for the RMSE value and prediction time, then compare it with the best average value. The results of the study show that the ARIMA model is able to predict electricity usage for the next 1 year period, in the evaluation using the RMSE metric, where SVM shows a much lower value than ARIMA and LSTM. In this case, SVM achieved RMSE of 0.020, while ARIMA and LSTM achieved RMSE of 7.659 and 11.4183, respectively. Even though SVM has a lower RMSE, it is still unable to predict electricity usage for the next 1 year with sufficient accuracy.