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Forecasting and assessing risk of in...

Jacob, Maria.

Forecasting and assessing risk of individual electricity peaks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Forecasting and assessing risk of individual electricity peaksby Maria Jacob, Claudia Neves, Danica Vukadinovic Greetham.
Author:	Jacob, Maria.
other author:	Neves, Claudia.
Published:	Cham :Springer International Publishing :2020.
Description:	xii, 97 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Electric power-plantsLoad
Online resource:	https://doi.org/10.1007/978-3-030-28669-9
ISBN:	9783030286699$q(electronic bk.)

Forecasting and assessing risk of individual electricity peaks
Jacob, Maria.

Forecasting and assessing risk of individual electricity peaks[electronic resource] /by Maria Jacob, Claudia Neves, Danica Vukadinovic Greetham. - Cham :Springer International Publishing :2020. - xii, 97 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in mathematics of planet earth, weather, climate, oceans,2509-7326. - SpringerBriefs in mathematics of planet earth, weather, climate, oceans..

Preface -- Introduction -- Short Term Load Forecasting -- Extreme Value Theory -- Extreme Value Statistics -- Case Study -- References -- Index.

Open access.

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.

ISBN: 9783030286699$q(electronic bk.)

Standard No.: 10.1007/978-3-030-28669-9doiSubjects--Topical Terms:

866249
Electric power-plants
--Load

LC Class. No.: TK1005

Dewey Class. No.: 621.31

Forecasting and assessing risk of individual electricity peaks
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520 $a 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.
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