Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 112 pp. Englisch.

N� de ref. del art�culo 9783030286682

Contactar al vendedor

Denunciar este art�culo

Detalles bibliogr�ficos

T�tulo: Forecasting and Assessing Risk of Individual Electricity Peaks
Editor: Springer International Publishing, Springer Nature Switzerland Okt 2019
Fecha de publicaci�n: 2019
Idioma: Ingl�s
ISBN 10: 3030286681
ISBN 13: 9783030286682
Encuadernaci�n: Taschenbuch
Estado: Neu

Acerca de

Sinopsis

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.

Acerca del autor

Maria Jacob completed a masters with the Mathematics of Planet Earth Centre for Doctoral training of University of Reading and Imperial College London. She is interested in using statistics and data science methods particularly within the public sector.

Cl�udia Neves is a Lecturer at the University of Reading. For over 10 years, her research in extreme value statistics has been informed as much as driven by a number of applications arising in hydrology (heavy rainfall) demography (supercentenarian’s lifespan), public health, and more recently, in the energy sector (e.g. electricity demand, safety issues in nuclear infrastructure). She has been awarded an EPRSC Innovation Fellowship for the project "Multivariate Max-stable Processes with Application to the Forecasting of Multiple Hazards".

Danica Vukadinović Greetham is Senior Research Fellow at the Open University’s Knowledge Media Institute. Her expertise is in network analysis and optimisationwith background in mathematics (BSc, University of Belgrade) and computer science (PhD, ETHZ) and over 15 years of industrial and academic experience. Her research interests include modelling and predicting human behaviour from big data, and mathematical modelling of low voltage networks.

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

Descripci�n de la librer�a

Impressum Thorsten Retsch Buchversand Mimpf2000 Ober�lschnitz 16 95517 Emtmannsberg Deutschland Telefon: 09209-2023188 Email: mimpf2000@online.de USt-ID-Nr.: DE 235096871 Wir f�hren gebrauchte B�cher aus allen Sparten der Literatur

Ver la p�gina web de la librer�a

Informaci�n empresarial del vendedor

buchversandmimpf2000
Germany

Condiciones de venta y env�o

Condiciones de venta

Widerrufsbelehrung/ Muster-Widerrufsformular/
Allgemeine Gesch�ftsbedingungen und Kundeninformationen/ Datenschutzerkl�rung

Widerrufsrecht f�r Verbraucher
(Verbraucher ist jede nat�rliche Person, die ein Rechtsgesch�ft zu Zwecken abschlie�t, die �berwiegend weder ihrer gewerblichen noch ihrer selbstst�ndigen beruflichen T�tigkeit zugerechnet werden k�nnen.)

Widerrufsbelehrung

Widerrufsrecht
Sie haben das Recht, binnen 14 Tagen ohne Angabe von Gr�nden diesen Vertrag zu widerrufen.
Die Widerrufsfr...

M�s informaci�n

Condiciones de env�o

Soweit in der Artikelbeschreibung keine andere Frist angegeben ist, erfolgt die Lieferung der Ware innerhalb von 3-5 Werktagen nach Vertragsschluss, bei Vorauszahlung erst nach Eingang des vollst�ndigen Kaufpreises und der Versandkosten. Alle Preise inkl. MwSt.

Informaci�n detallada sobre el vendedor