Forecasting and assessing risk of individual electricity peaks / Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham.
2020
TK1005
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Unlimited
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Can lend chapters, not whole ebooks
Details
Title
Forecasting and assessing risk of individual electricity peaks / Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham.
Author
Jacob, Maria.
ISBN
9783030286699 (electronic book)
303028669X (electronic book)
303028669X (electronic book)
Published
Cham : SpringerOpen, 2020.
Language
English
Description
1 online resource (xii, 97 pages) : illustrations
Item Number
10.1007/978-3-030-28 doi
Call Number
TK1005
Dewey Decimal Classification
621.31
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 4, 2019).
Added Author
Neves, Cláudia.
Vukadinović Greetham, Danica.
Vukadinović Greetham, Danica.
Series
SpringerBriefs in mathematics of planet Earth - weather, climate, oceans.
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Table of Contents
Preface
Introduction
Short Term Load Forecasting
Extreme Value Theory
Extreme Value Statistics
Case Study
References
Index.
Introduction
Short Term Load Forecasting
Extreme Value Theory
Extreme Value Statistics
Case Study
References
Index.