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Table of contents

  1. Front Matter
    Pages i-xii
  2. Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham
    Pages 1-14 Open Access
  3. Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham
    Pages 15-37 Open Access
  4. Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham
    Pages 39-60 Open Access
  5. Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham
    Pages 61-84 Open Access
  6. Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham
    Pages 85-96 Open Access
  7. Back Matter
    Pages 97-97

About this book

Introduction

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. 


Keywords

60G70, 05C85 , 62M10, 68T05 electricity forecasting extreme value theory scedasis heteroscedasticity short-term load forecast error measures permutation-based algorithms Block maxima methods in statistics of extremes individual electricity peaks risk of individual electricity peaks forecasting individual electricity peaks Open Access end-point estimation SARIMA models Long Short Term Memory (LSTM) Multi-layer Perceptron(MLP) permutation merge permutation-based errors Open Access

Authors and affiliations

  1. 1.University of ReadingReadingUK
  2. 2.Department of Mathematics and StatisticsUniversity of ReadingReadingUK
  3. 3.The Open UniversityMilton KeynesUK

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. 




Bibliographic information