基于非线性自回归神经网络的GHI预测

GHI FORECAST BASED ON NONLINEAR AUTOREGRESSIVE NEURAL NETWORK

针对水平面总辐照度(global horizontal irradiation,GHI)短期预测问题,提出一种基于非线性自回归神经网络的短期水平面太阳总辐照度预测模型。首先,提出一种并联结构训练样本,以保证训练样本内部的时间耦合性。其次,通过对9项气象参数共511种组合作为输入的模型预测精度进行分析,确定模型最优输入组合。最后,利用4种典型气象条件下GHI时延神经网络预测模型,非线性自回归动态神经网络预测模型预测标准均方根误差均降低。

This paper proposes a short-term GHI forecast model based on nonlinear autoregressive dynamic neural network. At first,this paper proposes a kind of training sample in parallel structure to guarantee the time correlation within training sample. Secondly,by comparing the forecast accuracy of 511 combinations of 9 meteorological parameters,as model inputs,the best input combination is identified. Finally,this paper tests model’s adaptiveness to four different typical weather conditions. By comparing with forecast results of the traditional forecast model based on focus time delay neural network,the forecast model based on nonlinear autoregressive neural network can effectively reduce the normalized root mean squared error.