Abstract
Abstract. Since the beginning of the 21st century, the scientific
community has made huge leaps to exploit renewable energy sources, with solar
radiation being one of the most important. However, the variability of solar
radiation has a significant impact on solar energy conversion systems, such
as in photovoltaic systems, characterized by a fast and non-linear response
to incident solar radiation. The performance prediction of these systems is
typically based on hourly or daily data because those are usually available
at these time scales. The aim of this work is to investigate the stochastic
nature and time evolution of the solar radiation process for daily and hourly
scale, with the ultimate goal of creating a new cyclostationary stochastic
model capable of reproducing the dependence structure and the marginal
distribution of hourly solar radiation via the clearness index KT.
Reference29 articles.
1. Aguiar, R. and Collares-Pereira, M. T. A. G.: TAG: a time-dependent, autoregressive, Gaussian model for generating synthetic hourly radiation, Sol. Energy, 49, 167–174, 1992.
2. Asquith, W. H.: Lmomco – L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions, R package version 2.3.1, Texas Tech University, Lubbock, Texas, USA, 2018.
3. Ayodele, T. R. and Ogunjuyigbe, A. S. O.: Prediction of monthly average global solar radiation based on statistical distribution of clearness index, Energy, 90, 1733–1742, 2015.
4. Burnham, K. P. and Anderson, D. R.: Model selection and multimodel inference: a practical information-theoretic approach, Springer Science & Business Media, New York, USA, 2003.
5. Csorgo, S. and Faraway, J. J.: The exact and asymptotic distributions of Cramér-von Mises statistics, J. Roy. Stat. Soc. B, 58, 221–234, 1996.
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