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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 729316, 14 pages
Research Article

Long Term Solar Radiation Forecast Using Computational Intelligence Methods

1ESTiG, Instituto Politécnico de Bragança, Campus de Santa Apolónia, Apartado 1134, 5301-857 Bragança, Portugal
2Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Portugal
3Universidade de Trás-os-Montes e Alto Douro (UTAD), Escola de Ciências e Tecnologia, Vila Real, Portugal
4INESC Technology and Science (INESC TEC), Portugal

Received 24 August 2014; Revised 14 November 2014; Accepted 17 November 2014; Published 11 December 2014

Academic Editor: Samuel Huang

Copyright © 2014 João Paulo Coelho and José Boaventura-Cunha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.