Ini adalah artikel publikasi pertama saya, yang merupakan bagian Tugas Akhir ketika mengikuti program Srata 1 di Jurusan Teknik Elektro Fakultas Teknik Universitas Andalas Padang. Tugas Akhir saya berjudul “Perbandingan Jaringan Syaraf Tiruan Model Statis dengan Model Dinamis untuk Peramalan Beban Jangka Pendek.” Artikel ini dipublikasikan pada Jurnal INVOTEK (Inovasi Vokasional dan Teknologi) Vol. IV No. 3 September 2003.

ABSTRACT

Short-term load forecasting is aimed at predicting electric energy that will consume for a period of hours or weeks to take a policy in controlling the schedule of the power system. Many methods have been done in this short-term load forecasting, such as by using Artificial Neural Network (ANN). The purpose of this research is to compare two models of ANN such as static and dynamic models that applied for short-term load forecasting. Training and testing data for static and dynamic models have been applied daily load data of West Sumatera subsystem that took at National Electric Power, Power System Load Units West Sumatera Riau Lubuk Alung. The result of testing during one week shown that average error for static ANN models and dynamic ANN models are 5.71975 % and 5.41366 %. Whereas average standard deviation for both ANN models is 10.9833 MW and 10.47560 MW. Base on it, if the level of error about  5 – 6 %  can be accepted, both of the models can be applied as an alternative solution in short-term load forecasting.

Keywords:  Artificial Neural Network (ANN), static model, dynamic model

How to cite:

APA 6th:

Effendi, H. (2003). Pemodelan jaringan syaraf tiruan untuk peramalan beban jangka pendek. INVOTEK: Jurnal Inovasi Vokasional dan Teknologi (Vol. IV, pp. 101-126). Padang: Fakultas Teknik, Universitas Negeri Padang.

IEEE:

[1] H. Effendi, “Pemodelan jaringan syaraf tiruan untuk peramalan beban jangka pendek,” in INVOTEK: Jurnal Inovasi Vokasional dan Teknologi vol. IV, ed. Padang: Fakultas Teknik, Universitas Negeri Padang, 2003, pp. 101-126.