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%.
Highlights
ABSTRAK Tenaga listrik merupakan salah satu kebutuhan utama masyarakat saat ini,mulai dari konsumen rumah tangga hingga industri
This study 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
This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024
Summary
Energi listrik merupakan salah satu kebutuhan utama masyarakat saat ini, mulai dari konsumen rumah tangga hingga industri. Jaringan syaraf ini dibentuk menggunakan fungsi Basis Radial sehingga disebut RBF Neural Network. Topologi jaringan dalam RBF terdiri dari 3 lapisan, yaitu lapisan input, lapisan atau unit tersembunyi dan lapisan keluaran. Setiap input dari jaringan syaraf tiruan RBF ini akan mengaktifkan semua fungsi dasar di lapisan tersembunyi. Setiap unit dari lapisan tersembunyi adalah fungsi aktivasi khusus yang disebut fungsi dasar. Keluaran jaringan ini adalah jumlah dari semua fungsi basis keluaran dikalikan dengan bobot masing-masing. Lapisan Tersembunyi Pada bagian ini kegiatan formulasi terjadi dalam pembentukan sistem algoritma yang digunakan dalam jaringan RBF. Lapisan Keluaran Hasil penjumlahan dari perkalian bobot dan fungsi dasar menghasilkan keluaran yaitu lapisan keluaran, yang merespons jaringan sesuai dengan pola. Langkah 3: Memberikan bobot (fungsi dasar) dari n + 1, di mana n adalah jumlah input RBF. Berikut jumlah konsumsi energi listrik Provinsi Jawa Tengah per Area tahun 2010 hingga tahun 2017
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