000 | 04082nam a22006375i 4500 | ||
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001 | 978-3-030-28669-9 | ||
003 | DE-He213 | ||
005 | 20220712124048.0 | ||
007 | cr nn 008mamaa | ||
008 | 190925s2020 sz | s |||| 0|eng d | ||
020 |
_a9783030286699 _9978-3-030-28669-9 |
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024 | 7 |
_a10.1007/978-3-030-28669-9 _2doi |
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050 | 4 | _aG70.23 | |
072 | 7 |
_aPBW _2bicssc |
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_aMAT003000 _2bisacsh |
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072 | 7 |
_aPBW _2thema |
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082 | 0 | 4 |
_a519 _223 |
100 | 1 |
_aJacob, Maria. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _970123 |
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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. |
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300 |
_aXII, 97 p. 40 illus., 36 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSpringerBriefs in Mathematics of Planet Earth, Weather, Climate, Oceans, _x2509-7334 |
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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 |
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650 | 0 |
_aStatistics . _929514 |
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650 | 0 | _aEnergy policy. | |
650 | 0 |
_aEnergy and state. _968807 |
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650 | 0 |
_aAlgorithms. _969497 |
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650 | 0 |
_aElectric power production. _979841 |
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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 |
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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 |
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912 | _aZDB-2-SXMS | ||
912 | _aZDB-2-SOB | ||
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