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Ramadani Dwisatya, Ramadani
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Prediksi Beban Listrik Jangka Pendek Menggunakan Algoritma Feed Forward Back Propagation dengan Mempertimbangkan Variasi Tipe Hari Dwisatya, Ramadani; Kirom, M.Ramdlan
ELECTRANS Vol 14, No 1 (2016): Volume 14, Nomor 1, Tahun 2016
Publisher : Universitas Pendidikan Indonesia

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The development of computing technology that has lead to soft computing technologies prompted researchers to try for finding an alternative method to predict the power load-based artificial intelligence (which is a popular and widely used: Adaptive Neural Network / Neural Network). Short term load forecasting has a very important role for the efficiency of electrical energy. For it will be done prediction electrical load short term for the 3 types of days, weekdays, weekends and national holidays by the method of Artificial Neural Network (ANN) algorithm using feedforward backpropagation, and the data used is real data throughout 2013 and 2014. The software for designing programs to use is Matlab from Mathwork Corps. Based on test results obtained average value error for all three types of day best is 2.89% and the forecasting from PLN gained 8.84% on the type of national holidays, so we get electrical energy efficiency on a national holiday the average 6% in each hour.