Abstract
A model for short-term forecasting of continuous time series has been developed. This model binds the use of both statistical and machine learning methods for short-time forecasting of continuous time series of solar radiation. The prediction of this variable is needed for the integration of photovoltaic systems in conventional power grids. The proposed model allows us to manage not only the information in the time series, but also other important information supplied by experts. In a first stage, we propose the use of statistical models to obtain useful information about the significant information for a continuous time series and then we use this information, together with machine learning models, statistical models and expert knowledge, for short-term forecasting of continuous time series. The results obtained when the model is used for solar radiation series show its usefulness.
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
Box, G.E.P., Jenkins, G.M.: Time Series Analysis forecasting and control. Prentice-Hall, USA (1976)
Brockwell, P.J., Richard A.D.: Introduction to Time Series and Forecasting. Springer Texts in Statistics (2002)
Peter Zhang, G., Qi, M.: Neural network forecasting for seasonal and trend time series. European Journal of Operational Research 160 (2005)
Wang, C.H., Hsu, L.C.: Constructing and applying an improved fuzzy time series model: Taking the tourism industry for example. Expert Systems with Applications 34 (2008)
Hwang, J., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems 100 (1998)
Tucker, A., Liu, X.: Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies. In: Berthold, M.R., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 100–110. Springer, Heidelberg (2003)
Ghahramani, Z., Hinton, G.E.: Variational Learning for Switching State-Space Models. Neural Computation 12(4), 831–864 (2000)
Dougherty, J., Kohavi, R., And Sahami, M.: Supervised and Unsupervised Discretization of Continuous Features. In: Proceedings of the Twelf International Conference on Machine Learning, pp. 194–202. Morgan Kaufmann, Los Altos (1995)
Lui, H., Hussain, F., Lim Tan, C., Dash, M.: Discretization: An enabling Technique. Data Mining and Knowledge Discovery 6, 393–423 (2002)
Boulle, M., Khiops: A Statistical Discretization Method of Continuous Attributes. Machine Learning 55, 53–69 (2004)
Aguiar, R., Collares-Pereira, M.: Statistical properties of hourly global radiation. Solar Energy 48(3), 157–167 (1992)
Seber, G.A.F., Lee, A.J.: Linear Regression Analysis, 2nd edn. Wiley, New Jersey (2003)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to The Theory of Neural Computation. Addison-Wesley Publishing Company, USA (1991)
Hassoun, M.H.: Fundamentals of Artificial Neural Networks. The MIT Press, USA (1995)
Anderson, J.A.: An Introduction to Neural Networks. The MIT Press, USA (1995)
Mora-López, L., Mora, J., Sidrach-de-Cardona, M., Morales-Bueno, R.: Modelling time series of climatic parameters with probabilistic finite automata. Environmental Modelling and Software 20(6) (2004)
Luque, A., Hegedus, S.: Handbook of Photovoltaic Science and Engineering. John Wiley and Sons Ltd., England (2003)
Kumar, R., Umanand, L.: Estimation of global radiation using clearness index model for sizing photovoltaic system. Renewable Energy 30(15) (2005)
Nakada, Y., Takahashi, H., Ichida, K., Minemoto, T., Takakura, H.: Influence of clearness index and air mass on sunlight and outdoor performance of photovoltaic modules. Current Applied Physics 10 (2,1) (2010)
Iqbal, M.: An introduction to solar radiation. Academic Press Inc., New York
Mora-López, L., Sidrach-de-Cardona, M.: Characterization and simulation of hourly exposure series of global radiation. Solar Energy 60(5), 257–270 (1997)
Ron, D., Singer, Y., Tishby, N.: Learning Probabilistic Automata with Variable Memory Length. In: Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mora-López, L., Martínez-Marchena, I., Piliougine, M., Sidrach-de-Cardona, M. (2011). Binding Statistical and Machine Learning Models for Short-Term Forecasting of Global Solar Radiation. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-24800-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24799-6
Online ISBN: 978-3-642-24800-9
eBook Packages: Computer ScienceComputer Science (R0)