基于多传感器系统的光伏并网发电功率预测

针对耦合性较强的多维气象数据,在光伏(PV)多传感器系统中获取有效数据的基础上,提出了一种基于雾霾因素影响的数据挖掘光伏发电预测方法.利用多传感器采集大数据,利用逐步选择法对多维变量进行约减,有效降低了不同天气因素之间的耦合性.通过混合高斯聚类算法对样本进行聚类,并分别建立不同的径向基函数(RBF)神经网络模型,经过模糊推理的方法选择适当模型,实际预测结果验证了方法的高精度和实用性.

Grid-connected PV power forecast based on MUSS

In view of multidimensional meteorological data with strong coupling,on the basis of obtaining valid data in photovoltaic multi-sensor system(MUSS),a data mining photovoltaic(PV)power generation forecasting method based on smog factor is proposed.Multi-sensor is used to collect big data,and stepwise selection method is used to select important variables to reduce the multidimensional variables,which effectively reduces the coupling between different weather factors.The samples are clustered by hybrid Gaussian clustering algorithm.According to the result of clustering algorithm,different radial basis function neural network(RBF)models are established, respectively.Fuzzy inference method is used to select the appropriate model.The actual prediction results validate the high precision and practicality of this method.