PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF)
DOI:
https://doi.org/10.26623/elektrika.v11i2.1699Keywords:
Artificial neural networks, electrical loads, predictions, radial basis function modelsAbstract
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%.Downloads
References
Arief, Hariyanto. 2005. Jaringan Syaraf Tiruan dan Teori Aplikasi Pemogramanya. Surabaya
Badan Pusat Statistik Jawa Tengah Dalam Angka 2010 2017
Data Kelistrikan PLN Tahun 2014, 2016 dan 2017
Hasan Basri, Ir.,2003. Teknik Distribusi Jaringan Listrik Menengah Dan Tegangan Tinggi. Jakarta
Hasim Agus. 2015 .Prakiraan Beban Listrik Kota Pontianak Dengan Jaringan Syaraf Tiruan. Skripsi Sekolah Pasca Sarjana Institut Pertanian Bogor.
Hermawan, 2008. Sistem Distribusi Listrik. Jakarta
J. Siang, Jak. 2005. Dasar Dasar Pengolahan Jaringan Syaraf Tiruan. Jakarta
Kuncoro Arief Heru dan Dalimi Rinaldy. 2005 .Aplikasi Jaringan Syaraf Tiruan Untuk Peramalan Beban Tenaga Listrik Jangka Panjang Pada Sistem Kelistrikan Di Indonesia. Jurnal Program Pasca Sarjana, Fakultas Teknik, Universitas Indonesia.
Puspitaningrum. 2006. Struktur Jaringan Syaraf Tiruan Backpropagation dan Radial Basis Function. Jakarta
Syeto Galang Jiwo, Fariza Arna, S.Kom, M.Kom, Setiawardhana. S.T. 2010 .Peramalan Beban Listrik Menggunakan Jaringan Syaraf Tiruan Metode Kohonen. Makalah Program DIV Jurusan Teknik Informatika, Politeknik Elektronika Negeri Surabaya-Institusi Teknologi Sepuluh Nopember.
S. N. Sivanandam, S. N Deepa.2006. Introduction to Neural Networks Using Matlab 6.0.New Delhi.
Downloads
Published
Issue
Section
License
Authors who publish this journal agree to the following terms:
The author owns the copyright and grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution 4.0 International License which allows others to share the work with recognition of the authorship of the work and initial publication in the journal.
Authors may enter into separate additional contractual agreements for non-exclusive distribution of the published journal version of the work (e.g., posting it to an institutional repository or publishing it in a book), in recognition of its initial publication in this journal.
Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their websites) before and during the submission process, as it can lead to productive exchanges, as well as earlier and larger citations of published works (See The Effects of Open Access).
This work is licensed under the Creative Commons Attribution 4.0 International License.
