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001 978-3-030-28669-9
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007 cr nn 008mamaa
008 190925s2020 sz | s |||| 0|eng d
020 _a9783030286699
_9978-3-030-28669-9
024 7 _a10.1007/978-3-030-28669-9
_2doi
050 4 _aG70.23
072 7 _aPBW
_2bicssc
072 7 _aMAT003000
_2bisacsh
072 7 _aPBW
_2thema
082 0 4 _a519
_223
100 1 _aJacob, Maria.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_970123
245 1 0 _aForecasting and Assessing Risk of Individual Electricity Peaks
_h[electronic resource] /
_cby Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXII, 97 p. 40 illus., 36 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Mathematics of Planet Earth, Weather, Climate, Oceans,
_x2509-7334
505 0 _aPreface -- Introduction -- Short Term Load Forecasting -- Extreme Value Theory -- Extreme Value Statistics -- Case Study -- References -- Index.
506 0 _aOpen Access
520 _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. .
650 0 _aGeography-Mathematics.
_9551374
650 0 _aStatistics .
_929514
650 0 _aEnergy policy.
650 0 _aEnergy and state.
_968807
650 0 _aAlgorithms.
_969497
650 0 _aElectric power production.
_979841
650 1 4 _aMathematics of Planet Earth.
_970126
650 2 4 _aStatistical Theory and Methods.
_970127
650 2 4 _aEnergy Policy, Economics and Management.
_968812
650 2 4 _aAlgorithms.
_969497
650 2 4 _aElectrical Power Engineering.
_91282360
650 2 4 _aMechanical Power Engineering.
_91282788
700 1 _aNeves, Cláudia.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_91282910
700 1 _aVukadinović Greetham, Danica.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_91282911
710 2 _aSpringerLink (Online service)
_923050
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030286682
776 0 8 _iPrinted edition:
_z9783030286705
830 0 _aSpringerBriefs in Mathematics of Planet Earth, Weather, Climate, Oceans,
_x2509-7334
_970132
856 4 0 _uhttps://doi.org/10.1007/978-3-030-28669-9
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
912 _aZDB-2-SOB
999 _c2855394
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