改进神经网络模型在光伏发电预测中的应用

针对传统的BP神经网络模型在光伏发电功率预测中，存在着的预测精度不高、收敛速度较慢的弊端，本文提出一种改进型的BP神经网络模型，运用增加动量项以及自适应选取最佳隐含层的方法来改进传统BP模型的缺陷.文中首先进行了各类气象因素对于光伏发电功率输出影响的相关性分析，提取出最能影响光伏发电功率的6个气象因素，作为网络模型的输入，然后建立了改进型的BP网络模型，结合光伏功率输出的历史数据，来进行发电数据的直接预测；最后根据不同气候类型下的预测结果，分析并验证了采用该模型进行功率预测的可行性和有效性.

Application of Improved Neural Network Model in Photovoltaic Power Generation Prediction

In view of the traditional BP neural network model used in Photo Voltaic (PV) power generation prediction, there are shortcomings of low prediction accuracy and slow convergence speed. In this study, an improved BP neural network model is proposed to improve the defects of traditional BP model by adding momentum term and adaptively selecting the best hidden layer. Firstly, the correlation of meteorological factors for PV power output is analyzed, and six meteorological factors that can affect the PV power are extracted as input of the network model. Then, an improved BP network model is established, combined with the historical data of PV power output, to directly predict the generation of data. Finally, according to the prediction results under different climate types, the feasibility and effectiveness of the model for power prediction are analyzed and verified.