LEADER 04201nam 22007815 450 001 9910299626403321 005 20200629162825.0 010 $a3-319-12319-X 024 7 $a10.1007/978-3-319-12319-6 035 $a(CKB)3710000000281316 035 $a(EBL)1967147 035 $a(OCoLC)895661112 035 $a(SSID)ssj0001386781 035 $a(PQKBManifestationID)11824980 035 $a(PQKBTitleCode)TC0001386781 035 $a(PQKBWorkID)11374466 035 $a(PQKB)11687292 035 $a(MiAaPQ)EBC1967147 035 $a(DE-He213)978-3-319-12319-6 035 $a(PPN)183091248 035 $a(EXLCZ)993710000000281316 100 $a20141114d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSpatio-Temporal Data Analytics for Wind Energy Integration /$fby Lei Yang, Miao He, Junshan Zhang, Vijay Vittal 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (86 p.) 225 1 $aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112 300 $aDescription based upon print version of record. 311 $a3-319-12318-1 320 $aIncludes bibliographical references. 327 $aIntroduction -- A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm Generation -- Support Vector Machine Enhanced Markov Model for Short-Term Wind Power Forecast -- Stochastic Optimization based Economic Dispatch and Interruptible Load Management -- Conclusions and Future Works. 330 $aThis SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic dispatch (ED) and interruptible load management are investigated as well. Spatio-Temporal Data Analytics for Wind Energy Integration is valuable for researchers and professionals working towards renewable energy integration. Advanced-level students studying electrical, computer and energy engineering should also find the content useful. 410 0$aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112 606 $aRenewable energy resources 606 $aData mining 606 $aEnergy policy 606 $aEnergy and state 606 $aEnergy systems 606 $aRenewable and Green Energy$3https://scigraph.springernature.com/ontologies/product-market-codes/111000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aEnergy Policy, Economics and Management$3https://scigraph.springernature.com/ontologies/product-market-codes/112000 606 $aEnergy Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/115000 615 0$aRenewable energy resources. 615 0$aData mining. 615 0$aEnergy policy. 615 0$aEnergy and state. 615 0$aEnergy systems. 615 14$aRenewable and Green Energy. 615 24$aData Mining and Knowledge Discovery. 615 24$aEnergy Policy, Economics and Management. 615 24$aEnergy Systems. 676 $a333.92 700 $aYang$b Lei$4aut$4http://id.loc.gov/vocabulary/relators/aut$0909422 702 $aHe$b Miao$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZhang$b Junshan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aVittal$b Vijay$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299626403321 996 $aSpatio-Temporal Data Analytics for Wind Energy Integration$92162321 997 $aUNINA