Wahab Musa, Salmawaty Tansa
Based on the rapid development of digital signal processing technology and computers, computational intelligence (CI) becomes an object of study fields of fundamental and applied research of interest to researchers recently. Research that exploits a number of further processing techniques are the subject of CI information technology, including artificial neural networks, genetic algorithms, fuzzy logic, evolutionary computation, or a combination of these techniques, known as hybrid technology. The purpose of this study is to develop a prediction model based CI to improve the accuracy of prediction of the demand for electricity. Method combination of several algorithms in search of optimum value will be developed to overcome the premature convergence on the model predictions. The data will be used to measure the performance of the hybrid model is data electrical energy needs of Indonesia. Average prediction errors will become a reference in selecting the right model for the planning of the electrical energy needs of the next few years in Indonesia. The results showed: 1) performance computational intelligence-based prediction models that utilize the capabilities of the hybrid algorithm is superior to the single algorithm-based predictive models. 2) The accuracy of the prediction model based hybrid algorithm (HAM) can reach 97%, exceeding the level of the previous model's accuracy. © 2005-2016 JATIT & LLS. All rights reserved.
Department of Electrical Engineering, Universitas Negeri Gorontalo, Indonesia
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