IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-97940-9_124.html
   My bibliography  Save this book chapter

Processing Smart Meter Data Using IoT, Edge Computing, and Big Data Analytics

In: Handbook of Smart Energy Systems

Author

Listed:
  • Mehar Ullah

    (Lappeenranta–Lahti University of Technology)

  • Annika Wolff

    (Lappeenranta–Lahti University of Technology)

  • Pedro H. J. Nardelli

    (Lappeenranta–Lahti University of Technology)

Abstract

Smart meters have the potential for improving the accuracy of demand forecasts and the energy efficiency, also allowing for the reduction in energy consumption. Mostly the smart meter data is used for the measurement of energy usage at the consumer side that is then sent to the utility providers for billing purposes and demand planning. The Internet of Things (IoT) is used to collect the data from smart meters, and that data is used for the calculations and visualization for smart grid maintenance and future decisions. The number of smart meters is constantly increasing and so is the data from those meters. Gathering and performing analytics on such data is a hard computational task. In this study, we have highlighted the role of IoT, edge computing, and big data and analytics focusing on how to speed up the information retrieval from the smart grid data and how that information can be used for multiple purposes, not only for billing purposes. A framework for more efficiently analyzing data obtained from smart meters is presented, which utilizes edge computing and big data and analytics to process raw data to useful information.

Suggested Citation

  • Mehar Ullah & Annika Wolff & Pedro H. J. Nardelli, 2023. "Processing Smart Meter Data Using IoT, Edge Computing, and Big Data Analytics," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 1987-2001, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_124
    DOI: 10.1007/978-3-030-97940-9_124
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-97940-9_124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.