# Forecasting and Assessing Risk of Individual Electricity Peaks

Part of the Mathematics of Planet Earth book series (MPE)

Also part of the SpringerBriefs in Mathematics of Planet Earth book sub series (SBMPE-WCO)

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Book
Part of the Mathematics of Planet Earth book series (MPE)

Also part of the SpringerBriefs in Mathematics of Planet Earth book sub series (SBMPE-WCO)

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.

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

- DOI https://doi.org/10.1007/978-3-030-28669-9
- Copyright Information The Editor(s) (if applicable) and The Author(s) 2020
- License CC BY
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics
- Print ISBN 978-3-030-28668-2
- Online ISBN 978-3-030-28669-9
- Series Print ISSN 2524-4264
- Series Online ISSN 2524-4272
- Buy this book on publisher's site