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Prediction of solar radiation using meteorological data

机译:利用气象数据预测太阳辐射

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Solar radiation prediction has a great importance in electricity generation from solar energy and helps to size photovoltaic power systems. Therefore, the solar radiation parameter was predicted at 10-min intervals in this study. Outside temperature, outside humidity and barometric pressure parameters were used as meteorological input variables by the developed k-nearest neighbor (k-NN) classifier. On the one hand, it is mined that solar radiation prediction was affected by the number of nearest neighbors, the dimension of input parameters and the type of distance metrics. On the other hand, it is shown that the k-NN classifier which uses Euclidean distance metric for k=4 in 3-dimensional input space outperformed the other models in terms of the prediction accuracy. Adversely, the k-NN classifier which only uses barometric pressure input provided the weakest prediction performance for k=15 in Euclidean distance metric.
机译:太阳辐射预测在利用太阳能发电方面具有非常重要的意义,并有助于确定光伏发电系统的规模。因此,在这项研究中以10分钟的间隔预测了太阳辐射参数。发达的k最近邻(k-NN)分类器将外部温度,外部湿度和大气压力参数用作气象输入变量。一方面,我们发现太阳辐射的预测受最近邻居的数量,输入参数的大小和距离度量的类型的影响。另一方面,示出了在3维输入空间中针对k = 4使用欧几里得距离度量的k-NN分类器在预测精度方面优于其他模型。相反,仅使用大气压力输入的k-NN分类器在欧氏距离度量中针对k = 15提供了最弱的预测性能。

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