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Vina Meilia
Brawijaya University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Extreme Learning Machine Weights Optimization Using Genetic Algorithm In Electrical Load Forecasting Vina Meilia; Budi Darma Setiawan; Nurudin Santoso
Journal of Information Technology and Computer Science Vol. 3 No. 1: June 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1812.596 KB) | DOI: 10.25126/jitecs.20183154

Abstract

The growth of electrical consumers in Indonesia continues to increases every year, but it is not matched by the provision of adequate infrastructure that available. This causes the available electrical capacity can't fulfill the demand for electricity.  In this study, a smart computing system is build to solves the problem. Electrical load data per hour is being used as an input to do the electrical load forecasting with Extreme Learning Machine method. Extreme Learning Machine method uses random input weight within range -1 to 1. Before the electric load prediction process runs, genetic algorithms first optimizing the input weight.  According to the test results with weight optimization, MAPE average error rate is 0.799% while without weight optimization the rate rise to 1.1807%. Thus this study implies that Extreme Learning Machine (ELM) method with weight optimization using Genetics Algorithm (GA) can be used in electrical load forecasting problem and give better prediction result
Co-Authors Budi Darma Setiawan Nurudin Santoso