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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Agarwal, M.: A systematic classification of neural-network-based control, IEEE Control Systems Mag. 17(2) (April 1997), 75-93.
Aguiar, R. and Collares-Pereira, M.: Statistical properties of hourly global radiation, Solar Energy 48 (1991), 157-167.
Aguiar, R. and Collares-Pereira, M.: TAG: A time-dependent, autoregressive, Gaussian model for generating synthetic hourly radiation, Solar Energy 49(3) (1992), 167-174.
Balouktsis, A. and Tsalides, Ph.: Stochastic simulation model of hourly total solar radiation, Solar Energy 37 (1986).
Barron, A. R.: Universal approximation bounds for superpositions of a sigmoidal function, IEEE Trans. Inform. Theory 39 (1993), 930-945.
Box, G. E. P. and Jenkins, G. M.: Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, 1970.
Brinkworth, B. J.: Autocorrelation and stochastic modelling of isolation sequences, Solar Energy 19 (1977).
Cybenko, G.: Approximation by superpositions of a sigmoidal function, Math. Control Signals Systems 2 (1989) 303-314.
Funahashi, K.: On the approximate realisation of continuous mappings by neural networks, Neural Networks 2 (1989), 183-192.
Goh, T. and Tan, K.: Stochastic modelling and forecasting of solar radiation data, Solar Energy 19 (1977).
Graham, V. A. and Hollands, K. G. T.: A method to generate synthetic hourly solar radiation globally, Solar Energy 44(6) (1990), 333-341.
Graham, V. A., Hollands, K. G. T., and Unny, T. E.: Stochastic variation of hourly solar radiation over the day, in: Proc. of the ISES Solar World Congress, Hamburg, 1987.
Graham, V. A., Hollands, K. G. T., and Unny, T. E.: A time series model for Kt with application to global synthetic weather generation, Solar Energy 40(3) (1988), 269-279.
Haykin, S.: Neural Networks. A Comprehensive Foundation, Macmillan, New York, 1994.
Hollands, K. and Hughet, R.: A probability density function for the clearness index with applications, Solar Energy 30 (1983), 195-209.
Hontoria, L., Riesco, J., Zufiria, P., and Aguilera, J., Improved generation of hourly solar irradiation artificial series using neural networks, in: EANN99, Varsovia, 1999.
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks 2(5) (1989), 359-366.
Hush, D. R. and Horne, B. G.: Progress in supervised neural networks. What's new since Lippmann, IEEE S.P. Mag. (January 1993), 8-39.
Knight, K. M., Klein, S. A., and Duffie, J. A.: A methodology for the synthesis of hourly weather data, Solar Energy 46(2) (1991), 109-120.
Kohonen, T.: Self-Organising Maps, Springer, Berlin/Heidelberg, 1995.
Lapedes, A. S. and Farber, R. M.: Non-linear signal processing using neural networks: Prediction and system modelling, Technical Report, Los Alamos National Laboratory, 1987.
Lippmann, R. P.: An introduction to computing with neural nets, IEEE ASSP Mag. (April 1987), 4-22.
Liu, B. and Jordan, R.: The interrelationship and characteristics distribution of direct, diffuse and total solar radiation, Solar Energy 4 (1960), 1-19.
Lorenzo, E.: Electricidad Solar Fotovoltaica, ETSI Telecomunicación (U.P.M. Madrid), 1991.
Mustacchi, C., Cena, V., and Rocchi, M.: Stochastic simulation of hourly global radiation sequences, Solar Energy 23 (1979).
Narendra, K. S. and Parthasarathy, K.: Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Networks 1(1) (March 1990), 4-27.
Narendra, K. S. and Parthasarathy, K.: Gradient methods for the optimisation of dynamical systems containing neural networks, IEEE Trans. Neural Networks 2(2) (March 1991), 252-262.
Oltseth, J. and Skartveit, A.: A probability density model for hourly total and beam irradiance on arbitrarily oriented planes, Solar Energy 39 (1987), 343-351.
Palomo, E.: Hourly solar radiation time series as first-order Markov chains, in: Proc. of the ISES Solar World Congress, Kobe, Japan, 1989.
Priestley, M. B.: Non-Linear and Non-Stationary Time Series Analysis, Academic Press, New York, 1988.
Rumelhart, D. and MacClelland, J. L.: Learning internal representations by error backpropagation, Parallel Distributed Processing, Vol. 1: Foundations, MIT Press, 1986, Chapter 8.
Vázquez-López, A. and Zufiria, P. J.: Generación artificial de series de radiación solar mediante perceptrón multicapa, in: Actas V Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 93), 16-18 Noviembre 1993, pp. 196-205.
Weigend, A. S., Rumelhart, D. E., and Huberman, B. A.: Back-propagation, weight-elimination and time series prediction, in: Proc. of the 1990 Connectionist Models Summer School, Morgan Kaufman, 1990.
Werbos, P.: Beyond regression: New tools for prediction and analysis in the behavioural sciences, PhD Dissertation, Harvard University, Cambridge, MA, November, 1974.
Zufiria, P. J.: An overview of dynamic system control using neural networks, in: A. B. Bulsari (ed.), Neural Networks for Chem. Engineers, Elsevier, Amsterdam, 1995, pp. 385-408.
Zufiria, P. J., Vázquez, A., Riesco, J. Aguilera, J., and Hontoria, L.: A neural network approach for generating solar irradiation artificial series, in: M. J. V. Sánchez-Andrés (ed.), Applications of Bio-Inspired Artificial Neural Networks. Work-Conference on Artificial and Natural Networks II, Notes in Computer Science 1607, Springer, Berlin/Heidelberg, 1999, pp. 874-883.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
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
Issue Date:
DOI: https://doi.org/10.1023/A:1012031827871