Logo Oapen
  • Search
  • Join
    • Deposit
    • For Librarians
    • For Publishers
    • For Researchers
    • Funders
    • Resources
    • OAPEN
    • For Librarians
    • For Publishers
    • For Researchers
    • Funders
    • Resources
    • OAPEN
    View Item 
    •   OAPEN Home
    • View Item
    •   OAPEN Home
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Forecasting and Assessing Risk of Individual Electricity Peaks

    Thumbnail
    Download PDF Viewer
    Web Shop
    Author(s)
    Jacob, Maria
    Neves, Cláudia
    Vukadinović Greetham, Danica
    Language
    English
    Show full item record
    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.
    URI
    http://library.oapen.org/handle/20.500.12657/23132
    Keywords
    Mathematics; Mathematics; Statistics ; Energy efficiency; Algorithms; Energy systems
    DOI
    10.1007/978-3-030-28669-9
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2020
    Series
    Mathematics of Planet Earth,
    Classification
    Numerical analysis
    Probability and statistics
    Applied mathematics
    Energy technology and engineering
    Pages
    97
    Rights
    https://creativecommons.org/licenses/by/4.0
    • Imported or submitted locally

    Browse

    All of OAPENSubjectsPublishersLanguagesCollections

    My Account

    LoginRegister

    Export

    Repository metadata
    Logo Oapen
    • For Librarians
    • For Publishers
    • For Researchers
    • Funders
    • Resources
    • OAPEN

    Newsletter

    • Subscribe to our newsletter
    • view our news archive

    Follow us on

    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

    OAPEN is based in the Netherlands, with its registered office in the National Library in The Hague.

    Director: Niels Stern

    Address:
    OAPEN Foundation
    Prins Willem-Alexanderhof 5
    2595 BE The Hague
    Postal address:
    OAPEN Foundation
    P.O. Box 90407
    2509 LK The Hague

    Websites:
    OAPEN Home: www.oapen.org
    OAPEN Library: library.oapen.org
    DOAB: www.doabooks.org

     

     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Differen formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    A logged-in user can export up to 15000 items. If you're not logged in, you can export no more than 500 items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.