From travel disruptions to natural disasters, extreme events have long captured the public’s imagination and attention. Due to their rarity and often associated calamity, they make waves in the news (Fig. 3.1) and stir discussion in the public realm: is it a freak event? Events of this sort may be shrouded in mystery for the general public, but a particular branch of probability theory, notably Extreme Value Theory (EVT), offers insight to their inherent scarcity and stark magnitude. EVT is a wonderfully rich and versatile theory which has already been adopted by a wide variety of disciplines in a plentiful way. From its humble beginnings in reliability engineering and hydrology, it has now expanded much further; it can be used to model the occurrences of records (say for example in athletic events) or quantify the probability of floods with magnitude greater than what has been observed in the past, i.e it allows us extrapolate beyond the range of available data! In this book, we are interested in what EVT can tell us about electricity consumption of individual households. We already know a lot about what regions and countries do on average but not enough about what happens at the substation level or at least not with enough accuracy. We want to consider “worst” case scenario such as an area-wide blackout or the “very bad” case scenario such as a circuit fuse blowout or a low-voltage event. Distribution System Operators (DSO) may want to know how much electricity they will need to make available for the busiest time of day up to two weeks in advance. Local councils or policy makers may want to decide if a particular substation is equipped to meet the demands of the residents and if it needs an upgrade or maintenance. EVT can definitely help us to answer some of these questions and perhaps even more as we develop and adapt the theory and approaches further. There are many ways to infer properties about a population based on various sample statistics. Depending on the statistic, a theory about how well it estimates
Extreme Value Theory
Published 2019 in Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management
ABSTRACT
PUBLICATION RECORD
- Publication year
2019
- Venue
Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management
- Publication date
2019-03-01
- Fields of study
Not labeled
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
Showing 1-70 of 70 references · Page 1 of 1