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Greenhouse Heat Load Prediction Using a Support Vector Regression Model

  • João Paulo Coelho
  • José Boaventura Cunha
  • Paulo de Moura Oliveira
  • Eduardo Solteiro Pires
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)

Abstract

Modern greenhouse climate controllers are based on models in order to simulate and predict the greenhouse environment behaviour. These models must be able to describe indoor climate process dynamics, which are a function of both the control actions taken and the outside climate. Moreover, if predictive or feedforward control techniques are to be applied, it is necessary to employ models to describe and predict the weather. From all the climate variables, solar radiation is the one with greater impact in the greenhouse heat load. Hence, making good predictions of this physical quantity is of extreme importance. In this paper, the solar radiation is represented as a time-series and a support vector regression model is used to make long term predictions. Results are compared with the ones achieved by using other type of models, both linear and non-linear.

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References

  1. 1.
    Cherkassky, V., Ma, Y.: Practical Selection of SVM parameters and Noise Estimation for SVM Regression. Neural Networks 17, 113–126 (2004)zbMATHCrossRefGoogle Scholar
  2. 2.
    Coelho, J., Cunha, J.B., Oliveira, P.B.: Greenhouse Air Temperature Control using the Particle Swarm Optimisation Algorithm. COMPAG - Computers and Electronics in Agriculture 49(3), 330–344 (2005)CrossRefGoogle Scholar
  3. 3.
    Coelho, J., Cunha, J.B., Oliveira, P.B.: Solar radiation prediction using wavelet decomposition. In: 8th Portuguese Conference on Automatic Control - CONTROLO 2008 (2008)Google Scholar
  4. 4.
    Gunn, S.R.: Support vector machines for classification and regression. Technical report. University of Southampton (1998)Google Scholar
  5. 5.
    Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of 2nd Berkeley Symposium, pp. 481–492. University of California Press, Berkeley (1951)Google Scholar
  6. 6.
    Müller, K.-R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting Time Series with Support Vector Machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  7. 7.
    Van Straten, G.: On-line optimal control of greenhouse crop cultivation. Acta Hort. 406, 203–212 (1996)Google Scholar
  8. 8.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, New York (1995)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • João Paulo Coelho
    • 1
  • José Boaventura Cunha
    • 2
  • Paulo de Moura Oliveira
    • 2
  • Eduardo Solteiro Pires
    • 3
  1. 1.CITAB - Centro de Investigação e de Tecnologias Agro-Ambientais e BiológicasInstituto Politécnico de BragançaBragançaPortugal
  2. 2.Dep. EngenhariasUniversidade de Trás-os-Montes e Alto DouroVila RealPortugal
  3. 3.CITAB - Centro de Investigação e de Tecnologias Agro-Ambientais e BiológicasUniversidade de Trás-os-Montes e Alto Douro, Dep. EngenhariasVila RealPortugal

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