SHORT TERM FORECASTING BEBAN LISTRIK MENGGUNAKAN ARTIFICIAL NEURAL NETWORK - Muhtar, Muhdalifah; Novie Ayub Windarko; Setiawardhana; Kadek Reda Setiawan Suda (Jurnal Pendidikan Teknologi dan Kejuruan, 2023)
In Masamba City, the use of electrical energy is influenced by the welfare of the population. The higher the level of welfare of the population, the greater the use of electricity. So, power plants must be ready to supply electricity load demand when experiencing sudden fluctuations in electricity demand. One way that needs to be done to deal with this is structured planning and forecasting. This study aims to analyze the results of the error value of the ability to forecast electrical loads using the Artificial Neural Network method. In this study, the method used is a quantitative approach where data collection is done by literature study, observation, and direct interviews at the UP3 Palopo office. The Artificial Neural Network (ANN) method is programmed using MATLAB software. Where forecasting using ANN obtained the smallest error value of 0.83% with an estimated power generated by ANN of 35,991 MVA on day 2 and for the largest error value on day 7 with an error value of 8.33% with estimated power generated by ANN of 36.0836 MVA.