PERAMALAN BEBAN LISTRIK JANGKA MENENGAH PADA SISTEM KELISTRIKAN KOTA SAMARINDA

Abstract

Demand of electric power in Samarinda continuously increasing in line with development of Samarinda city. To fill the demand of electricity in the future at a certain period, it is necessary to know precisely the demand for electricity in the certain period. This research has been carried out mid-term electric load forecasting electricity system in Samarinda using Artificial Neural Network (ANN). This method is an excellent method for finding non-linear relationship between load with economic factors are varied, and can make adjustments to the changes.The result of this study indicates that the selection of parameters such as the learning method, the number of neurons, hidden layer and influence the accuracy of forecasting the electrical load. From the results of electric power load forecasting medium term Samarinda MSE values obtained by 6,9134E + 03, using the parameters training and network configuration [7-70-1]. Retrieved peak load in 2020 amounted to 741 MW, close to the electrical plan of PT. PLN (Persero) amounting to 718 MW. In the electricity load forecasting is well known that the annual burden will increase.

Keywords

• forecasting
• load power
• Artificial Neural Network