Title :
Based on improved BP neural network model generating power predicting for PV system
Author :
Xiaobo Duan ; Lei Fan
Author_Institution :
Hebei Electric Power Research Institute, Shijiazhuang, China 050021
Abstract :
In this work the artificial neural networks (ANN) and the optimization algorithm of nonlinear damping least squares (Levenberg-Marquardt) were applied to estimate the generating power of photovoltaic system in China. And the MATLAB was applied to establish prediction model. Finally, the training samples were measured data of 30 days, 90 days and 180 days. Under the three samples, it researched generating output power forecasting of photovoltaic system. The predicted results show LMBP overcomes the shortcomings of BP neural network; it has better convergence and accuracy. After compare with all predicting data, predicting results of 30 days data is the most accurate among three training samples.
Keywords :
BP arithmetic; gennerating power prediction; neural network; photovoltaic system;
Conference_Titel :
World Automation Congress (WAC), 2012
Conference_Location :
Puerto Vallarta, Mexico
Print_ISBN :
978-1-4673-4497-5