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Nonlinear modeling of solar radiatio...

Fortuna, Luigi.

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Nonlinear modeling of solar radiation and wind speed time series

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear modeling of solar radiation and wind speed time series/ by Luigi Fortuna, Giuseppe Nunnari, Silvia Nunnari.
Author:	Fortuna, Luigi.
other author:	Nunnari, Giuseppe.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xv, 98 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Time-series analysis. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-38764-2
ISBN:	9783319387642

Nonlinear modeling of solar radiation and wind speed time series
Fortuna, Luigi.

Nonlinear modeling of solar radiation and wind speed time series[electronic resource] /by Luigi Fortuna, Giuseppe Nunnari, Silvia Nunnari. - Cham :Springer International Publishing :2016. - xv, 98 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in energy,2191-5520. - SpringerBriefs in energy..

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.

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.

ISBN: 9783319387642

Standard No.: 10.1007/978-3-319-38764-2doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Nonlinear modeling of solar radiation and wind speed time series
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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.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xv, 98 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in energy, $x 2191-5520
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.
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650 0 $a Solar radiation $x Mathematical models. $3 2189047
650 0 $a Winds $x Speed $x Mathematical models. $3 2199981
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650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
700 1 $a Nunnari, Giuseppe. $3 2199979
700 1 $a Nunnari, Silvia. $3 2199980
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