Modelling the distribution of solar spectral irradiance using data mining techniques
Modelling the distribution of solar spectral irradiance using data mining techniques
复制标题
使用数据挖掘技术对太阳光谱辐照度分布进行建模
DOI：
10.1016/j.envsoft.2013.12.002
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Llanos Mora López
中科院分区：
文献类型：
--
作者：
R. M. Sáez;Llanos Mora López
提出了一种模拟太阳光谱辐照度分布的方法。它同时使用统计和数据挖掘技术。结果表明，利用一些天文参数和气象参数太阳辐照度、温度和湿度可以模拟太阳光谱辐照度分布。利用这些参数，估算了表征太阳光谱的平均光子能量和归一化因子。首先，使用Kolmogorov-Smirnov两样本检验对所有测量光谱进行分析和比较。随后使用k-均值数据挖掘技术对所有测量结果进行分类。我们发现，三个星团足以描述所有观测到的光谱。最后，用人工神经网络和多元线性回归来模拟与特定气象参数匹配的太阳光谱分布。结果表明，测量光谱的累积概率分布函数与模拟谱的累积概率分布函数有99.98%以上的一致性。
A procedure for modelling the distribution of solar spectral irradiance is proposed. It uses both statistical and data mining techniques. As a result, it is possible to simulate solar spectral irradiance distribution using some astronomical parameters and the meteorological parameters solar irradiance, temperature and humidity. With these parameters, the average photon energy and the normalization factor, which characterise the solar spectra, are estimated. First, the Kolmogorov–Smirnov two-sample test is used to analyse and compare all measured spectra. The k-means data mining technique is subsequently used to cluster all measurements. We found that three clusters are enough to characterise all observed spectra. Finally, an artificial neural network and a multivariate linear regression are estimated to simulate the solar spectral distribution matching certain meteorological parameters. The results obtained show that over 99.98% of cumulative probability distribution functions of measured spectra are the same as simulated ones.