Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review
International Journal of Artificial Intelligence and Soft Computing
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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.