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100%: Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham: Forecasting and Assessing Risk of Individual Electricity Peaks (SpringerBriefs in Mathematics of Planet Earth) (English Edition) (ISBN: 9783030286699) 2019, Springer, Erstausgabe, in Deutsch, auch als eBook.Nur diese Ausgabe anzeigen…
71%: Maria Jacob; Cláudia Neves; Danica Vukadinović Greetham: Forecasting and Assessing Risk of Individual Electricity Peaks Author (ISBN: 9783030286682) in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
65%: Danica Vukadinovic Greetham, Claudia Neves, Maria Jacob: Forecasting and Assessing Risk of Individual Electricity Peaks (Paperback) (ISBN: 9781013273780) Saint Philip Street Press, United States, in Englisch, Taschenbuch.Nur diese Ausgabe anzeigen…
65%: Danica Vukadinovic Greetham/ Cláudia Neves/ Maria Jacob: Forecasting and Assessing Risk of Individual Electricity Peaks (ISBN: 9781013273797) in Englisch, Broschiert.Nur diese Ausgabe anzeigen…
Forecasting and Assessing Risk of Individual Electricity Peaks (SpringerBriefs in Mathematics of Planet Earth) (English Edition)
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Forecasting and Assessing Risk of Individual Electricity Peaks (Paperback) (2020)
~EN PB NWISBN: 9781013273780 bzw. 1013273788, vermutlich in Englisch, Saint Philip Street Press, United States, Taschenbuch, neu.
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Free shipping.
Von Händler/Antiquariat, Book Depository hard to find [63688905], London, United Kingdom.
Language: English. Brand new Book. The 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. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Von Händler/Antiquariat, Book Depository hard to find [63688905], London, United Kingdom.
Language: English. Brand new Book. The 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. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
2
Forecasting and Assessing Risk of Individual Electricity Peaks
~EN PB NWISBN: 9783030286682 bzw. 3030286681, vermutlich in Englisch, Springer Shop, Taschenbuch, neu.
Lieferung aus: Mexiko, Lagernd, zzgl. Versandkosten.
The 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. Soft cover.
The 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. Soft cover.
3
Forecasting and Assessing Risk of Individual Electricity Peaks Maria Jacob Author
~EN PB NWISBN: 9783030286682 bzw. 3030286681, vermutlich in Englisch, Springer International Publishing, Taschenbuch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, zzgl. Versandkosten.
The 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.
The 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.
4
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Forecasting and Assessing Risk of Individual Electricity Peaks (2020)
~EN NW RPISBN: 9781013273780 bzw. 1013273788, vermutlich in Englisch, Saint Philip Street Press, neu, Nachdruck.
Von Händler/Antiquariat, Books2Anywhere [190245], Fairford, GLOS, United Kingdom.
New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Forecasting and Assessing Risk of Individual Electricity Peaks (2017)
~EN PB NW RPISBN: 9781013273780 bzw. 1013273788, vermutlich in Englisch, Saint Philip Street Press, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, Ria Christie Collections [59718070], Uxbridge, United Kingdom.
PRINT ON DEMAND Book; New; Publication Year 2017; Fast Shipping from the UK.
PRINT ON DEMAND Book; New; Publication Year 2017; Fast Shipping from the UK.
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Forecasting and Assessing Risk of Individual Electricity Peaks (SpringerBriefs in Mathematics of Planet Earth) (English Edition) (2019)
DE NW FE EB DLISBN: 9783030286699 bzw. 303028669X, in Deutsch, Springer, neu, Erstausgabe, E-Book, elektronischer Download.
Lieferung aus: Deutschland, E-Book zum Download, Versandkostenfrei.
Von Händler/Antiquariat, Amazon Media EU S.à r.l.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Von Händler/Antiquariat, Amazon Media EU S.à r.l.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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Forecasting and Assessing Risk of Individual Electricity Peaks
~EN HC NWISBN: 1013273796 bzw. 9781013273797, vermutlich in Englisch, Saint Philip Street Press, gebundenes Buch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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Forecasting and Assessing Risk of Individual Electricity Peaks
~EN PB NWISBN: 1013273788 bzw. 9781013273780, vermutlich in Englisch, Saint Philip Street Press, Taschenbuch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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Forecasting Assessing Risk of Individual Electricity Peaks (2020)
~EN PB NWISBN: 9781013273780 bzw. 1013273788, vermutlich in Englisch, Saint Philip Street Press, Taschenbuch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, plus shipping, Shipping area: STOCKNEW.
Von Händler/Antiquariat, Alibris, NV, Sparks, [RE:5].
Trade paperback.
Von Händler/Antiquariat, Alibris, NV, Sparks, [RE:5].
Trade paperback.
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