基于神经网络的光伏电站气象-功率模型

基于双层前馈神经网络建立光伏电站输出功率与辐照等气象因素间的非机理模型。建立光伏电站输出功率与气象因素的神经网络模型；对功率模型的输入特征进行选择，分析不同气象因素的组合作为输入变量对模型准确度的影响，明确功率模型的输入变量；分析该模型网络的训练算法、隐含层神经元个数及训练次数对模型准确度的影响，据此确定功率模型的最优结构与参数；基于光伏电站的实际数据对功率模型进行验证。结果表明，基于双层前馈神经网络的光伏电站气象-功率模型具有较高的准确度。

Neural network based model of photovoltaic output power-weather information

In this study, a non-mechanism model for the output power of photovoltaic(PV)plant considering weather factors such as irradiance, etc. was established based on a two-layer feed-forward neural network. Firstly, a neural network-based model of output power of PV power plant was established by using weather factors as the inputs. Secondly, the combination of input features for the neural network model was selected. The impacts of different weather factors combinations to the model accuracy were compared to select the input combination of the power model. Then, the training algorithm, the number of hidden layer neurons, and the training times, were changed in the neural network to compare the estimation accuracy and simulation time, and thus the optimal network configuration and parameters of the power model were determined. Finally, the optimized power model of the PV power plant was validated based on the actual measured data. The result shows that the proposed power model has high accuracy.