Identifying statistical properties of solar radiation models by using information criteria

Abstract : The purpose of this article is to improve modeling of solar irradiance through the analysis of measurement data on the ground in the intertropical zone. For this, we identify, using information criteria, the probabilistic distributions introduced in two models of synthetic solar irradiance generation. We then validate the results by using the KL divergence and KSI parameter as comparison criteria between distributions arising from real and synthesized data. Our study confirms, for example, that the Gaussian classical distribution is not suitable for modeling solar irradiance, and we propose other more suitable statistical laws instead. The value of the identification procedure of the distribution laws presented in this article is that it ensures the production of solar irradiance data comparable in their statistical content to the measured data. Another advantage is that this procedure contributes to highlighting the time invariance of distribution laws representing the random terms. We conclude that this new information-criteria-based method permits the identification of the probability laws that best describe the statistical distributions introduced in models of synthetic solar irradiance generation.
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Laurent Linguet, Yannis Pousset, Christian Olivier. Identifying statistical properties of solar radiation models by using information criteria. Solar Energy, Elsevier, 2016, 132, pp.236-246. ⟨10.1016/j.solener.2016.02.038 ⟩. ⟨meteo-01304572⟩

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