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A new Procedure to Generate Solar Radiation Time Series from achine Learning Theory

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Modeling Solar Radiation at the Earth’s Surface

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References

  • Aguiar RJ, Collares-Pereira M, Conde JP (1988) Simple procedure for generating sequences of daily radiation values using a library of Markov Transition Matrix. Solar Energy 40(3):269–279.

    Article  Google Scholar 

  • Aguiar RJ, Collares-Pereira M (1992) Tag: A time-dependent, autoregressive, gaussian model for generating synthetic hourly radiation. Solar Energy 49(3):167–174.

    Article  Google Scholar 

  • Bendt P, Collares-Pereira M, Rabl A (1981) The frequency distribution of daily insolation values. Solar Energy 27:1–5.

    Article  Google Scholar 

  • Box, GEP, Jenkins GM (1976) Time series analysis forecasting and control. USA: Prentice-Hall.

    MATH  Google Scholar 

  • Brinkworth FJ (1977) Autocorrelation and stochastic modelling of insolation series. Solar Energy 19:343–347.

    Article  Google Scholar 

  • Forbus KD (1984) Qualitative process theory. Artificial Intelligence 24:85–168.

    Article  Google Scholar 

  • Kemmoku Y, Orita S, Nakagawa S and Sakakibara T (1999). Daily insolation forecasting using a multi-stage neural network. Solar Energy 66(3):193–199.

    Article  Google Scholar 

  • Kleer J, Brown JS (1984) A qualitative physics based on confluences. Artificial Intelligence 24:7–83.

    Article  Google Scholar 

  • Krog A, Mian SI and Haussler D (1993) A hidden Markov model that finds genes in E.coli DNA. Technical report UCSC-CRL-93-16, University of California at Santa-Cruz.

    Google Scholar 

  • Kuipers B (1984) Commonsense reasoning about causality deriving behavior from structure. Artificial Intelligence 24:169–203.

    Article  Google Scholar 

  • Manzanares Badía, Juan Manuel (2006) Herramienta para el análisis multivariante utilizando técnicas de aprendizaje automático y modelos estadísticos. Proyecto fin de carrera. E.T.S.I. Informática. Universidad de Málaga.

    Google Scholar 

  • Mohandes M, Balghonaim M, Kassas M, Rehman S and Halawani TO (2000) Use of radial basis functions for estimating monthly mean daily solar radiation. Solar Energy 68(2):161.

    Article  Google Scholar 

  • Mohandes M, Rehman S and Halawani TO (1998) Estimation of global solar radiation using artificial neural networks. Renewable Energy 14(1–4):179–184.

    Article  Google Scholar 

  • Mora-López L, Fortes I, Morales-Bueno R and Triguero F (2000) Dynamic discretization of continuous values from time series. Lecture Notes in Artificial Intelligence 1810:280–291.

    Google Scholar 

  • Mora-López L, Morales-Bueno R, Sidrach-de-Cardona M, Triguero F (2002) Probabilistic Finite Automata and Randomness in Nature: a New Approach in the Modelling and Prediction of Climatic Parameters. Proceeding of the International Environmental Modelling and Software Society Congress. Lugano, Suiza, June 2002.

    Google Scholar 

  • Mora-López L, Sidrach-de-Cardona M (1997) Characterization and simulation of hourly exposure series of global radiation. Solar Energy 60(5):257–270.

    Article  Google Scholar 

  • Nadas A (1984). Estimation of probabilities in the language model of the IBM speech recognition system. IEEE Trans. on ASSP 32(4):859–861.

    Article  Google Scholar 

  • Rabiner LR (1994) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the Seventh Annual Workshop on Computational Learning Theory, 1994.

    Google Scholar 

  • Rissanen J (1983) A universal data compression system. IEEE Trans. Inform. Theory 29(5):656–664.

    Article  MATH  MathSciNet  Google Scholar 

  • Rohatgi VK (1976) An Introduction to Probability Theory and Mathematical Statistics. John Wiley & Sons, USA.

    MATH  Google Scholar 

  • Ron D, Singer Y and Tishby N (1998) On the learnability and usage of acyclic probabilistic finite Automata. Journal of Computer and System Sciences 56:133–152.

    Article  MATH  MathSciNet  Google Scholar 

  • Ron D, Singer Y and Tishby N (1994) Learning probabilistic automata with variable memory length. Proceedings of the Seventh Annual Workshop on Computational Learning Theory.

    Google Scholar 

  • Sfetsos A, Coonick AH (2000) Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy 68(2):169–178.

    Article  Google Scholar 

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Mora-López, L. (2008). A new Procedure to Generate Solar Radiation Time Series from achine Learning Theory. In: Badescu, V. (eds) Modeling Solar Radiation at the Earth’s Surface. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77455-6_12

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  • DOI: https://doi.org/10.1007/978-3-540-77455-6_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77454-9

  • Online ISBN: 978-3-540-77455-6

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