Abstract
In this paper, a neural network method for generating solar radiation synthetic series is proposed and evaluated. In solar energy application fields such as photovoltaic systems and solar heating systems, the need of long sequences of solar irradiation data is fundamental. Nevertheless those series are not frequently available: in many locations the records are incomplete or difficult to manage, whereas in other places there are no records at all. Hence, many authors have proposed different methods to generate synthetic series of irradiation trying to preserve some statistical properties of the recorded ones. The neural procedure shown here represents a simple alternative way to address this problem. A comparative study of the neural-based synthetic series and series generated by other methods has been carried out with the objective of demonstrating the universality and generalisation capabilities of this new approach. The results show the good performance of this irradiation series generation method.
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Hontoria, L., Aguilera, J., Riesco, J. et al. Recurrent Neural Supervised Models for Generating Solar Radiation Synthetic Series. Journal of Intelligent and Robotic Systems 31, 201–221 (2001). https://doi.org/10.1023/A:1012031827871
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DOI: https://doi.org/10.1023/A:1012031827871