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Based On Improved BP Neural Network Model Generating Power Predicting For PV System

机译:基于改进的BP神经网络模型的光伏发电系统功率预测

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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.
机译:本文利用人工神经网络(ANN)和非线性阻尼最小二乘法的优化算法(Levenberg-Marquardt)估算了中国光伏系统的发电量,并利用MATLAB建立了预测模型。最后,训练样本是30天,90天和180天的测量数据。在这三个样本下,研究了光伏系统的发电输出功率预测。预测结果表明,LMBP克服了BP神经网络的缺点。它具有更好的收敛性和准确性。与所有预测数据进行比较后,三个训练样本中30天数据的预测结果最为准确。

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