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

Abstract

Electricity consumption \xa0in Indonesia is expected to continue to grow by average of 6,5% per year until 2020. Therefore, PT. PLN had to\xa0 make an effective subsystem that can provide \xa0electrical energy \xa0based on customer needs . The electrical energy is\xa0 converted from \xa0the mechanical energy\xa0 and \xa0can’t be stored. Because of that reason , \xa0if \xa0the\xa0 electrical energy isn’t channeled properly then PT. PLN will suffer losses. It is necessary to plan a proper distribution system\xa0 of \xa0electrical 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 \xa0sample . The result of the analysis shows that Multilayer Perc e ptron method provides better accuracy rate for electricity consumption forecasting in Paiton subsystem based on peak load compared\xa0 to \xa0ARIMA