Extreme Learning Machine Weights Optimization Using Genetic Algorithm In Electrical Load Forecasting - Meilia, Vina; Setiawan, Budi Darma; Santoso, Nurudin (Faculty of Computer Science (FILKOM) Brawijaya University, 2018)
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.Â 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.Â 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