Comparison Analysis of Electricity Load Demand Prediction using Recurrent Neural Network (RNN) and Vector Autoregressive Model (VAR)

Abstract

Electricity plays a very important role in daily modern human-life activities. An electricity company must always guarantee the continuity and adequate supply to its customers. Consequently, it must always be able to predict the future electricity demand to be supplied by considering various influencing factors. Many forecasting methods have been investigated and proposed by researchers to help in predicting the future electricity demand to be fulfilled, which is a paramount information in planning the transmission and distribution infrastructure and the generation plants to be built. In this study, two forecasting methods are described, explored and compared to provide alternative consideration in choosing the method. An artificial intelligence-based forecasting method, the Recurrent Neural Network (RNN), is to be compared to a conventional forecasting method, the Vector Autoregressive (VAR). The comparison is based on the parameters of Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). Both methods are implemented to predict the shortterm electricity load demand in Malang City, the second largest city after Surabaya in East Java province of Indonesia. The existing load data have been obtained from local electricity company, whereas the weather data have been taken from the Meteoblue Climatology NOAA. The architecture modelling of the RNN and VAR methods are performed in such a way to produce an accurate forecasting result. Based on the RMSE and MAE values, the prediction results of short-term electricity load in Malang city using the RNN method with hidden neuron variations indicate the lower values of RMSE and MAE, indicating better accuracy and performance, than the use of the VAR method with lag value variation.