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

Mehar Ullah (), Annika Wolff and Pedro H. J. Nardelli
Additional contact information
Mehar Ullah: Lappeenranta–Lahti University of Technology
Annika Wolff: Lappeenranta–Lahti University of Technology
Pedro H. J. Nardelli: Lappeenranta–Lahti University of Technology

A chapter in Handbook of Smart Energy Systems, 2023, pp 1987-2001 from Springer

Abstract: 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.

Keywords: Internet of Things; Big data; Edge computing; Edge analytics; Advanced metering infrastructure; Smart meters (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_124

Ordering information: This item can be ordered from
http://www.springer.com/9783030979409

DOI: 10.1007/978-3-030-97940-9_124

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().