Articles liés à Forecasting and Assessing Risk of Individual Electricity...
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
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.
Cláudia 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 Vukadinovic 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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 24,73
EUR 3,41 expédition vers Etats-Unis
Destinations, frais et délaisAutres éditions populaires du même titre
Résultats de recherche pour Forecasting and Assessing Risk of Individual Electricity...
Image fournie par le vendeur
Forecasting and Assessing Risk of Individual Electricity Peaks (Mathematics of Planet Earth)
Edité par
Springer, 2019
ISBN 10 : 3030286681
ISBN 13 : 9783030286682
Ancien ou d'occasion
Couverture souple
Vendeur : WeBuyBooks, Rossendale, LANCS, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. N° de réf. du vendeur wbb0024197842
Quantité disponible : 1 disponible(s)
Image d'archives
Forecasting and Assessing Risk of Individual Electricity Peaks (SpringerBriefs in Mathematics of Planet Earth)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur ABLIING23Mar3113020012404
Quantité disponible : Plus de 20 disponibles
Image d'archives
Forecasting and Assessing Risk of Individual Electricity Peaks (Mathematics of Planet Earth)
Vendeur : Chiron Media, Wallingford, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Paperback. Etat : New. N° de réf. du vendeur 6666-IUK-9783030286682
Quantité disponible : 10 disponible(s)
Image d'archives
Forecasting and Assessing Risk of Individual Electricity Peaks (SpringerBriefs in Mathematics of Planet Earth)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9783030286682_new
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Forecasting and Assessing Risk of Individual Electricity Peaks
Edité par
Springer International Publishing Okt 2019, 2019
ISBN 10 : 3030286681
ISBN 13 : 9783030286682
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 112 pp. Englisch. N° de réf. du vendeur 9783030286682
Quantité disponible : 2 disponible(s)
Image d'archives
Forecasting and Assessing Risk of Individual Electricity Peaks
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : Brand New. 97 pages. 9.00x6.00x0.50 inches. In Stock. N° de réf. du vendeur x-3030286681
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Forecasting and Assessing Risk of Individual Electricity Peaks
Edité par
Springer International Publishing, 2019
ISBN 10 : 3030286681
ISBN 13 : 9783030286682
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a self-contained theory and algorithms for individual energy load peak predictionImplementations are available in Python in RUses case studies on publicly available data and has accessible chapters with examples on extreme v. N° de réf. du vendeur 448677885
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Forecasting and Assessing Risk of Individual Electricity Peaks
ISBN 10 : 3030286681
ISBN 13 : 9783030286682
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. 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 réf. du vendeur 9783030286682
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Forecasting and Assessing Risk of Individual Electricity Peaks
ISBN 10 : 3030286681
ISBN 13 : 9783030286682
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783030286682
Quantité disponible : 1 disponible(s)