LEADER 03883nam 22006015 450 001 9910253983003321 005 20220411233235.0 010 $a3-319-38764-2 024 7 $a10.1007/978-3-319-38764-2 035 $a(CKB)3710000000734722 035 $a(DE-He213)978-3-319-38764-2 035 $a(MiAaPQ)EBC4561886 035 $a(PPN)194379906 035 $a(EXLCZ)993710000000734722 100 $a20160621d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNonlinear modeling of solar radiation and wind speed time series /$fby Luigi Fortuna, Giuseppe Nunnari, Silvia Nunnari 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XV, 98 p. 57 illus., 49 illus. in color.) 225 1 $aSpringerBriefs in Energy,$x2191-5520 311 $a3-319-38763-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aTime-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. 330 $aThis brief is a clear, concise description of the main techniques of time series analysis ?stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.? as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques. The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques. Results are well-illustrated by figures and tables. 410 0$aSpringerBriefs in Energy,$x2191-5520 606 $aRenewable energy resources 606 $aPower electronics 606 $aStatistics 606 $aRenewable and Green Energy$3https://scigraph.springernature.com/ontologies/product-market-codes/111000 606 $aPower Electronics, Electrical Machines and Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24070 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 615 0$aRenewable energy resources. 615 0$aPower electronics. 615 0$aStatistics. 615 14$aRenewable and Green Energy. 615 24$aPower Electronics, Electrical Machines and Networks. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a519.55 700 $aFortuna$b Luigi$4aut$4http://id.loc.gov/vocabulary/relators/aut$0759658 702 $aNunnari$b Giuseppe$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aNunnari$b Silvia$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910253983003321 996 $aNonlinear Modeling of Solar Radiation and Wind Speed Time Series$92294355 997 $aUNINA