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

• Published in: Computational Intelligence and Neuroscience Vol. 2017; no. 2017; pp. 1 - 14
• Main Authors: Gao, Xiang-ming, Pan, Sanbo, Yang, Shi-feng
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2017; Hindawi Publishing Corporation; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Alternative energy; Analysis; Animals; Bees - physiology; Biomimetics; Case studies; Collaboration; Computer engineering; Computer Systems; Decomposition; Fault diagnosis; Forecasting - methods; Genetic algorithms; Machine learning; Mathematical models; Mathematical optimization; Methods; Neural networks; Optimization algorithms; Photovoltaic cells; Photovoltaic power generation; Prediction models; Prediction theory; Search algorithms; Short term; Solar cells; Support Vector Machine; Support vector machines; Swarm intelligence; Time series; Traffic control; Weather; Weather forecasting; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2017/7273017

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.