rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Forecasting and Assessing Risk of Individual Electricity Peaks
  name:
    name part: Jacob, Maria.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Neves, Cláudia.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Vukadinović Greetham, Danica.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XII, 97 pages 40 illustrations, 36 illustrations in color.)
  abstract: 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. .
  table of contents: Preface -- Introduction -- Short Term Load Forecasting -- Extreme Value Theory -- Extreme Value Statistics -- Case Study -- References -- Index.
  note: by Maria Jacob, Cláudia Neves, Danica Vukadinović Greetham.
  note: Open Access
  subject:
    topic: Mathematics
  subject:
    topic: Statistics
  subject:
    topic: Energy efficiency
  subject:
    topic: Algorithms
  subject:
    topic: Energy systems
  subject:
    topic: Mathematics of Planet Earth
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Energy Efficiency
  subject:
    topic: Algorithms
  subject:
    topic: Energy Systems
  classification: NX180.M33
  classification: 519
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: SpringerBriefs in Mathematics of Planet Earth, Weather, Climate, Oceans
  identifier: 9783030286699
  identifier: https://doi.org/10.1007/978-3-030-28669-9
  access condition: Open Access
  record info:
    record creation date: 190925
    record change date: 20230829015248.2
    record identifier: in00004381064