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Cahyo, Mukti Dwi
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Electrical & Electronics Engineering

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PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF) Cahyo, Mukti Dwi; Heranurweni, Sri; Harmini, Harmini
Elektrika Vol. 11 No. 2 (2019): Oktober 2019
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (696.005 KB) | DOI: 10.26623/elektrika.v11i2.1699

Abstract

Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.
Co-Authors Harmini, Harmini Sri Heranurweni