LEADER 04593 am 22007933u 450 001 996418258003316 005 20230126214953.0 010 $a3-030-28669-X 024 7 $a10.1007/978-3-030-28669-9 035 $a(CKB)4100000009382559 035 $a(DE-He213)978-3-030-28669-9 035 $a(MiAaPQ)EBC5941331 035 $a(Au-PeEL)EBL5941331 035 $a(OCoLC)1135670157 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/38099 035 $a(PPN)242823491 035 $a(EXLCZ)994100000009382559 100 $a20190925d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aForecasting and Assessing Risk of Individual Electricity Peaks$b[electronic resource] /$fby Maria Jacob, Cláudia Neves, Danica Vukadinovi? Greetham 205 $a1st ed. 2020. 210 $aCham$cSpringer Nature$d2020 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 97 p. 38 illus., 35 illus. in color.) 225 1 $aSpringerBriefs in Mathematics of Planet Earth, Weather, Climate, Oceans,$x2509-7326 311 $a3-030-28668-1 327 $aPreface -- Introduction -- Short Term Load Forecasting -- Extreme Value Theory -- Extreme Value Statistics -- Case Study -- References -- Index. 330 $aThe overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general. . 410 0$aSpringerBriefs in Mathematics of Planet Earth, Weather, Climate, Oceans,$x2509-7326 606 $aMathematics 606 $aStatistics  606 $aEnergy efficiency 606 $aAlgorithms 606 $aEnergy systems 606 $aMathematics of Planet Earth$3https://scigraph.springernature.com/ontologies/product-market-codes/M36000 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aEnergy Efficiency$3https://scigraph.springernature.com/ontologies/product-market-codes/118000 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 606 $aEnergy Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/115000 610 $aMathematics 610 $aStatistics  610 $aEnergy efficiency 610 $aAlgorithms 610 $aEnergy systems 615 0$aMathematics. 615 0$aStatistics . 615 0$aEnergy efficiency. 615 0$aAlgorithms. 615 0$aEnergy systems. 615 14$aMathematics of Planet Earth. 615 24$aStatistical Theory and Methods. 615 24$aEnergy Efficiency. 615 24$aAlgorithms. 615 24$aEnergy Systems. 676 $a519 700 $aJacob$b Maria$4aut$4http://id.loc.gov/vocabulary/relators/aut$0985113 702 $aNeves$b Cláudia$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aVukadinovi? Greetham$b Danica$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418258003316 996 $aForecasting and Assessing Risk of Individual Electricity Peaks$92251279 997 $aUNISA