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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >The Generalized Additive Model for the Assessment of the Direct, Diffuse, and Global Solar Irradiances Using SEVIRI Images, With Application to the UAE
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The Generalized Additive Model for the Assessment of the Direct, Diffuse, and Global Solar Irradiances Using SEVIRI Images, With Application to the UAE

机译：利用SEVIRI图像评估直接，漫射和全球太阳辐照度的广义加法模型，并应用于阿联酋

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摘要

Generalized additive models (GAMs) can model the nonlinear relationship between a response variable and a set of explanatory variables through smooth functions. GAM is used to assess the direct, diffuse, and global solar components in the United Arab Emirates (UAE), a country which has a large potential for solar energy production. Six thermal channels of the spinning enhanced visible and infrared imager (SEVIRI) instrument onboard Meteosat second generation (MSG) are used as explanatory variables along with the solar zenith angle, solar time, day number, and eccentricity correction. The proposed model is fitted using reference data from three ground measurement stations for the full year of 2010 and tested on two other stations for the full year of 2009. The performance of the GAM model is compared to the performance of the ensemble of artificial neural networks (ANNs) approach. Results indicate that GAM leads to improved estimates for the testing sample when compared to the bagging ensemble. GAM has the advantage over ANN-based models that we can explicitly define the relationships between the response variable and each explanatory variable through smooth functions. Attempts are made to provide physical explanations of the relations between irradiance variables and explanatory variables. Models in which the observations are separated as cloud-free and cloudy and treated separately are evaluated along with the combined dataset. Results indicate that no improvement is obtained compared to a single model fitted with all observations. The performance of the GAM is also compared to the McClear model, a physical-based model providing estimates of irradiance in clear sky conditions.
机译：通用加性模型（GAM）可以通过平滑函数对响应变量和一组解释变量之间的非线性关系进行建模。 GAM用于评估阿拉伯联合酋长国（UAE）的直接，扩散和全球太阳能组件，该国具有巨大的太阳能生产潜力。 Meteosat第二代（MSG）上的旋转增强型可见光和红外成像仪（SEVIRI）仪器的六个热通道与太阳天顶角，太阳时间，天数和偏心率校正一起用作解释变量。该模型使用来自三个地面测量站的2010年全年的参考数据进行拟合，并在其他两个站点的2009年全年进行了测试。将GAM模型的性能与人工神经网络集成的性能进行了比较（ANN）方法。结果表明，与套袋相比，GAM可以提高测试样品的估计值。与基于ANN的模型相比，GAM的优势在于我们可以通过平滑函数显式定义响应变量和每个解释变量之间的关系。试图对辐照度变量和解释变量之间的关系提供物理解释。将观察结果分为无云和阴天并分别处理的模型与组合数据集一起进行评估。结果表明，与装有所有观察值的单个模型相比，没有任何改善。 GAM的性能也与McClear模型进行了比较，后者是一种基于物理的模型，可以提供晴朗天空条件下的辐照度估算值。

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