ESTIMASI BEBAN PUNCAK ENERGI LISTRIK PADA SISTEM SULUTGO MENGGUNAKAN ARTIFICIAL NEURAL NETWORK DAN METODE MOVING AVERAGE - Liberty Tarigan; Tritiya Arungpadang; Johan S C Neyland (Fakultas Teknik Jurusan Teknik Mesin Universitas Sam Ratulangi, 2016)
Sulutgo interconnection system is the electrical energy suppliers for North Sulawesi and Gorontalo. Their role as the electrical energy supplier was complained by people in 2015, due to lack of electricity supply that lead to continuous rolling blackouts. Accordingly, it is important to identify the electrical peak load in Sulutgo system, so that the electrical necessity of the people can be properly fulfilled. The electrical peak load in the next 12 month is estimated using the backpropagation method artificial neural network and forecasting method moving average. The estimation was performed by using the last 24 month peak load data. Based on the results of both estimation, it is found the backpropagation method artificial neural network has fluctuated results while the forecasting method moving average gives stable results. The results of the estimation of peak load electricity using bacpropagationÂ artificial neural network method for the next 12 month starting from July 2016 to June 2017 are 327.48, 353.99, 316.32, 316.66, 332.37, 329.79, 332.31, 356.21, 318.60, 349.56, 351.37, 362.04 MW. While the results of the estimation method using moving average forecasting for the same period are 325.68, 326.03, 326.39, 326.72, 327.25, 328.09, 327.94, 328.72, 329.94, 330.32, 327.65, 326.52 MW. Â Keywords: Estimation, Artificial Neural Network, Forecast Method Moving Average