基于RBF神经网络的太阳能发电量预测

太阳能发电系统输出功率的预测，尤其是短期预测，受到各种外源因素的影响，预测结果并不理想。由于现有预测方法对外部条件的依赖性，如太阳辐射强度和环境温度等，致使存在多种变量，影响预测的效率。为此，采用径向基函数神经网络(RBFN)，选择最优的输入参数，来预测20 kW太阳能电池板的功率输出，考虑效率的同时，对预测结果有了一定程度的改善。并将所提出方法与现有方法进行了比较。

Solar Power Prediction Based on RBF Neural Network

The prediction of the output power of solar power generation system, especially the short-term prediction, is not ideal due to the influence of various exogenous factors. Due to the dependence of existing prediction methods on external conditions, such as solar radiation intensity and ambient temperature, there are many variables that affect the efficiency of prediction. Therefore, the radial basis function neural network (RBFN) is used to select the optimal input parameters to predict the power output of 20 kW solar panels. Considering the efficiency, the prediction results are improved to a certain extent. The proposed method is compared with existing methods.