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Computational Intelligence and Neuroscience
Volume 2017, Article ID 7273017, 14 pages
Research Article

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

1School of Physics and Electrical Engineering, Anyang Normal University, Anyang 455000, China
2College of Electronic Information and Automation, Tianjin University of Science & Technology, Tianjin 300222, China

Correspondence should be addressed to Xiang-ming Gao; moc.361@oagcbca

Received 20 March 2017; Revised 14 June 2017; Accepted 27 June 2017; Published 22 August 2017

Academic Editor: José Alfredo Hernández-Pérez

Copyright © 2017 Xiang-ming Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.