Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model\nfor grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM)\noptimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date,\nthe time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output\npower are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component\nRes, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend\ncomponent, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results\nof each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained.\nThe prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABCSVM\nhas a faster calculation speed and high
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