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Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction
Joint Authors
Pan, Sanbo
Gao, Xiang-ming
Yang, Shi-feng
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-22
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gao, Xiang-ming& Yang, Shi-feng& Pan, Sanbo. 2017. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1141067
Modern Language Association (MLA)
Gao, Xiang-ming…[et al.]. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1141067
American Medical Association (AMA)
Gao, Xiang-ming& Yang, Shi-feng& Pan, Sanbo. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1141067
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141067