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Forecasting and Assessing Risk of Individual Electricity Peaks

Forecasting and Assessing Risk of Individual Electricity Peaks

by Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham


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Book Description

The overarching aim of this 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. You can download Forecasting and Assessing Risk of Individual Electricity Peaks ebook for free in PDF format (5.2 MB).

Table of Contents

Chapter 1
Introduction
1
Chapter 2
Short Term Load Forecasting
15
Chapter 3
Extreme Value Theory
39
Chapter 4
Extreme Value Statistics
61
Chapter 5
Case Study
85
Index
97

Book Details

Publisher
Springer
Published
2020
Pages
97
Edition
1
Language
English
ISBN13
9783030286682
ISBN10
3030286681
eISBN13
9783030286699
eISBN10
303028669X
PDF size
5.2 MB
License
CC

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