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Nonlinear Modeling of Solar Radiation and Wind Speed Time Series

This brief is a clear, concise description of the main techniques of time series analysis -stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.- as they are applied to the influence of wind speed and solar radiation on the production of electrical...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Fortuna, Luigi (Autor), Nunnari, Giuseppe (Autor), Nunnari, Silvia (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:SpringerBriefs in Energy,
Temas:
Acceso en línea:Texto Completo

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100 1 |a Fortuna, Luigi.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Nonlinear Modeling of Solar Radiation and Wind Speed Time Series  |h [electronic resource] /  |c by Luigi Fortuna, Giuseppe Nunnari, Silvia Nunnari. 
250 |a 1st ed. 2016. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2016. 
300 |a XV, 98 p. 57 illus., 49 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a SpringerBriefs in Energy,  |x 2191-5539 
505 0 |a Time-Series Methods -- Analysis of Solar-Radiation Time Series -- Analysis of Wind-Speed Time Series -- Prediction Models for Solar-Radiation and Wind-Speed Time Series -- Modeling Hourly Average Solar-Radiation Time Series -- Modeling Hourly Average Wind-Speed Time Series -- Clustering Daily Solar-Radiation Time Series -- Clustering Daily Wind-Speed Time Series -- Concluding Remarks. Appendix: List-of-Functions. 
520 |a This brief is a clear, concise description of the main techniques of time series analysis -stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.- as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques. The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques. Results are well-illustrated by figures and tables. 
650 0 |a Renewable energy sources. 
650 0 |a Electric power production. 
650 0 |a Statistics . 
650 1 4 |a Renewable Energy. 
650 2 4 |a Electrical Power Engineering. 
650 2 4 |a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 
700 1 |a Nunnari, Giuseppe.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Nunnari, Silvia.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319387635 
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