Abstract
In this work a neural network technique is applied to estimate hourly values of diffuse solar radiation at the surface in São Paulo City, Brazil, using as input global solar radiation and other meteorological parameters measured from 1998 to 2002. The diffuse solar radiation at the surface was directly estimated using a shadowring device. All available measurements are used to train a Perceptron neural network based model.
Feature determination techniques are used to select the optimal subset of meteorological parameters. Pattern selection techniques are employed to determine the most suitable subset of historical measurements for effective neural network based model training.
The results obtained here indicated the possibility of effective use of neural network based models for several tasks of pre-processing meteorological parameters when explicit formulas are not known. The models are especially useful for problems involving parameters in highly non linear relationship.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Božnar, M., 1997, Pattern selection strategies for a neural network — based short term air pollution prediction model. V: ADELI, H., ed., Intelligent Information Systems IIS’97, Grand Bahama Island, Bahamas, December 8–10, 1997. Proceedings. Los Alamitos, California: IEEE Computer Society, 340-344
Božnar, Marija Zlata, MLAKAR, Primož. Artificial neural network-based environmental models. V: GRYNING, Sven-Erik (ur.), SCHIERMEIER, Francis A. (ur.). Air pollution modeling and its application XIV. New York [etc.]: Kluwer Academic: Plenum Publishers, cop. 2001, 483–490.
Božnar, Marija Zlata, MLAKAR, Primož. Use of neural networks in the field of air pollution modelling. V: 25th NATO/CCMS International Technical Meeting on Air Pollution Modelling and its Application, 15–19 October 2001, Aula Magna, Louvain-la-Neuve, Belgium. Preprints. Louvain: Université Catholique de Leuvain, 2002, 265-272.
Božnar, M., Lesjak, M., Mlakar, P., 1993, A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmos. environ., B Urban atmos., vol. 27, 221–230.
CETESB, 1999: Technical Report (in Portuguese).
Collares-Pereira, M. and Rabl, A. (1979) The average distribution of solar radiation — correlation between diffuse and hemispherical and between daily and hourly insolation values, Solar Energy, 22, 155–164.
Eletropaulo, 1999: Technical Report (in Portuguese).
Hornik, K., 1991, Approximation capabilities of multilayer feedforward networks, Neural Networks, vol. 4, 251–257
Gardner, M.W. and Dorling, S.R., 1998, Artificial Neural Networks (The Multilayer Perceptron)-A Review of Applications in the Atmospheric Sciences. Atmospheric Environment, 32(14/15), 2627–2636
Iziomon, M.G. and Aro, T.O., 1999: On the Annual and Monthly Mean Diurnal Variation of Diffuse Solar Radiation at a Meteorological Station in West Africa. Meteorol. Atmos. Phys., 69, 223–230.
Kretzschmar, J. G., 1994: Particulate matter levels and trends in Mexico City, São Paulo, Buenos Aires and Rio de Janeiro. Atmos. Environ., 28, 3181–3191.
Lawrence, J., 1991, Introduction to Neural Networks, California Scientific Software, Grass Valley
LeBaron, B. A., Michalsky, J.J. and Perez, R., 1990: A simple procedure for correcting shadowband data for all sky conditions. Solar Energy, 44, 249–256.
Mlakar, P., 1997, Determination of features for air pollution forecasting models, V: ADELI, H., ed., Intelligent Information Systems IIS’97, Grand Bahama Island, Bahamas, December 8–10, Proceedings. Los Alamitos, California: IEEE Computer Society, 350–354.
Mlakar, P., Božnar, M., 1996, Analysis of winds and SO2 concentrations in complex terrain. V: Caussade, B. ed., Power, H. ed., Brebbia, C. A., ed., Air pollution IV: monitoring, simulation and control. Southampton; Boston: Computational Mechanics Publications, 455–464.
Oliveira, A. P., Escobedo, J.F, Plana-Fattori, A., Soares, J. and Santos, P. M., 1996: Medidas de radiaçào solar na Cidade de São Paulo: Calibraçäo de piranômetros e aplicações meteorológicas. Revista Brasileira de Geofisica, 14, 203–216. (in Portuguese)
Oliveira, A. P., A.J. Machado, J.F. Escobedo and J. Soares, 2002a: Diurnal evolution of solar radiation at the surface in the city of São Paulo: seasonal variation and modeling. Theoretical and Applied Climatology. 71 (3–4). 231–249.
Oliveira, A. P. Machado, A J. and J. F. Escobedo 2002b: Diurnal evolution of the solar radiation in the City of São Paulo, Brazil: Seasonal Variation and Modeling, Meteorology and Atmospheric Physics.
Oliveira, A.P., A.J. Machado, J.F. Escobedo, J. Soares, 2002c: Correlation models of diffuse solar radiation applied to the city of São Paulo (Brazil). Applied Energy. 71(1), 59–73
Pereira, E.B., Abreu, S. L., Stuhlmann, R., Reiland, M. and Colle, S. (1996) Survey of the incident solar radiation in Brazil by use of Meteosat satellite data, Solar Energy, 2, 125–132.
Rumelhart, D. E., McClelland, J. L., 1986, Parallel distributed processing 1,2, MIT Press, Cambridge, MA
WMO, 1971: Guide to meteorological instruments and observing practices. Fourth Edition, Geneva, Secretariat of the World Meteorological Organization, publication WMO N∘ 8, 325 pp.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer Science+Business Media New York
About this paper
Cite this paper
Božnar, M.Z., Mlakar, P., Oliveira, A.P., Soares, J. (2004). Modelling Diffuse Solar Radiation in the City of SÃO Paulo Using Neural-Network Technique. In: Borrego, C., Incecik, S. (eds) Air Pollution Modeling and Its Application XVI. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8867-6_28
Download citation
DOI: https://doi.org/10.1007/978-1-4419-8867-6_28
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-4697-5
Online ISBN: 978-1-4419-8867-6
eBook Packages: Springer Book Archive