Prakiraan Beban Puncak Pada Transformator GITET 150 kV Kesugihan Cilacap Menggunakan Jaringan Syaraf Tiruan Multilayer Feedforward Dengan Algoritma Backpropagation - Dimas Aditia Dicki; Winarso Winarso (Proceedings Series on Physical & Formal Sciences, 2021)
The increasing population and the growth of the industrial world, offices, hotels, and modern markets must be directly proportional to Indonesia's availability of electrical energy. The availability of sufficient electrical energy can affect the quality of life of the people and foster investor confidence in our country. Studies on the prediction (estimation) of peak electrical loads in electricity in Indonesia can be carried out using the Artificial Neural Network (ANN) method. The estimation of electricity load for the next 5 years is strongly influenced by several parameters, including population growth and peak load data of 150 kV GITET, Kesugihan Cilacap. This study took population data and peak load data at GITET 150 KV Kesugihan Cilacap in the past 5 years. The data used in this study were actual data, starting from 2015 to 2019. To display the results of the estimated electrical load on the 150 kV GITET transformer, the authors used the artificial neural network method. The peak electrical loads estimation results using artificial neural networks for electricity loads in the next 5 years, to wit from 2020 - 2024. The estimated peak load in Lomanis District is20.0311 MW, 24.443 MW, 19.9707 MW, 19.9705 MW and 19, 9705 MW. In Gombong District, the estimated peak load is 57,398 MW, 57,472 MW, 57,476 MW, 57,474 MW, and 57,479 MW.