TY  - BOOK
AU  - Jacob,Maria
AU  - Neves,Cláudia
AU  - Vukadinović Greetham,Danica
ED  - SpringerLink (Online service)
TI  - Forecasting and Assessing Risk of Individual Electricity Peaks
T2  - SpringerBriefs in Mathematics of Planet Earth, Weather, Climate, Oceans,
SN  - 9783030286699
AV  - G70.23 
U1  - 519 23
PY  - 2020///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Geography
KW  - Mathematics
KW  - Statistics 
KW  - Energy policy
KW  - Energy and state
KW  - Algorithms
KW  - Electric power production
KW  - Mathematics of Planet Earth
KW  - Statistical Theory and Methods
KW  - Energy Policy, Economics and Management
KW  - Electrical Power Engineering
KW  - Mechanical Power Engineering
N1  - Preface -- Introduction -- Short Term Load Forecasting -- Extreme Value Theory -- Extreme Value Statistics -- Case Study -- References -- Index; Open Access
N2  - 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.
UR  - https://doi.org/10.1007/978-3-030-28669-9
ER  -