Short Term Forecasting of Electricity Load: A Comparison of Methods to Paiton Subsystem East Java & Bali

Abstract

Electricity consumption in Indonesia is expected to continue to grow by average of 6,5% per year until 2020. Therefore, PT. PLN had to make an effective subsystem that can provide electrical energy based on customer needs. The electrical energy is converted from the mechanical energy and can’t be stored. Because of that reason, if the electrical energy isn’t channeled properly then PT. PLN will suffer losses. It is necessary to plan a proper distribution system of electrical energy. The aim of this research is to predict short-term electricity consumption for Paiton’s subsystem in East Java Indonesia by using ARIMA and Multilayer Perceptron. The best model is measured based on MAPE, SMAPE, and RMSE value in data sample. The result of the analysis shows that Multilayer Perceptron method provides better accuracy rate for electricity consumption forecasting in Paiton subsystem based on peak load compared to ARIMA