Anomalous STLF for Indonesia power system using artificial neural network

Y. Mulyadi; L. Farida; A.G. Abdullah; K.A. Rohmah

doi:10.1109/TICST.2015.7369331

← Back to search

Anomalous STLF for Indonesia power system using artificial neural network

Closed

Y. Mulyadi, L. Farida, A.G. Abdullah, K.A. Rohmah

2015 Proceedings 2015 International Conference on Science and Technology, TICST 2015 Conference paper Cited by 4

Abstract

This paper presents the research results of Short Term Load Forecasting (STLF) on the power distribution systems in the West Java, Indonesia. Forecasting is executed using Artificial Neural Network (ANN), with back propagation algorithms. Experiments conducted on the data load holidays (anomalous load). To obtain optimal prediction accuracy, then conducted the experiment by changing the number of input learning and learning rate value. The simulation results verify that the ANN method performs more accurate than the conventional method used Indonesia Power Company. Results of this study are expected to be used as an alternative method based on soft computing. © 2015 IEEE.

Affiliations

Electrical Power Systems Research Group, Departement of Electrical Engineering Education, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No. 207, Bandung, 40154, Indonesia

View full text (DOI) →

Research at a Glance

Research at a Glance

Topics & SDG Alignment

Collaboration

Author Profile (Selected)

References Overview

Journal & Source

Metadata & Integrity