Get 40% off select print & eBooks in Engineering & Materials or 50% off eBooks in Medicine & Psychology!

SpringerBriefs in Mathematics of Planet Earth
Open Access This content is freely available online to anyone, anywhere at any time.

Forecasting and Assessing Risk of Individual Electricity Peaks

Authors: Jacob, Maria, Neves, Cláudia, Vukadinovic Greetham, Danica

Free Preview
  • Presents a self-contained theory and algorithms for individual energy load peak prediction
  • Implementations are available in Python in R
  • Uses case studies on publicly available data and has accessible chapters with examples on extreme value theory and statistics
  • Open Access
  •  
see more benefits

Buy this book

eBook  
  • ISBN 978-3-030-28669-9
  • This book is an open access book, you can download it for free on link.springer.com
Softcover 19,99 €
price for Korea, Republic of (South Korea) (gross)
  • ISBN 978-3-030-28668-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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. 


About the authors

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 Vukadinović Greetham is Senior Research Fellow at the Open University’s Knowledge Media Institute. Her expertise is in network analysis and optimisation with 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. 




Table of contents (5 chapters)

Table of contents (5 chapters)
  • Introduction

    Pages 1-14

    Jacob, Maria (et al.)

  • Short Term Load Forecasting

    Pages 15-37

    Jacob, Maria (et al.)

  • Extreme Value Theory

    Pages 39-60

    Jacob, Maria (et al.)

  • Extreme Value Statistics

    Pages 61-84

    Jacob, Maria (et al.)

  • Case Study

    Pages 85-96

    Jacob, Maria (et al.)

Buy this book

eBook  
  • ISBN 978-3-030-28669-9
  • This book is an open access book, you can download it for free on link.springer.com
Softcover 19,99 €
price for Korea, Republic of (South Korea) (gross)
  • ISBN 978-3-030-28668-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Forecasting and Assessing Risk of Individual Electricity Peaks
Authors
Series Title
SpringerBriefs in Mathematics of Planet Earth
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s)
eBook ISBN
978-3-030-28669-9
DOI
10.1007/978-3-030-28669-9
Softcover ISBN
978-3-030-28668-2
Series ISSN
2509-7326
Edition Number
1
Number of Pages
XII, 97
Number of Illustrations
3 b/w illustrations, 35 illustrations in colour
Topics