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Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Author

Listed:
  • Benjamin Völker

    (Chair of Computer Architecture, University of Freiburg, 79110 Freiburg, Germany)

  • Andreas Reinhardt

    (Department of Informatics, TU Clausthal, 38678 Clausthal-Zellerfeld, Germany)

  • Anthony Faustine

    (Center for Artificial Intelligence (CeADAR), University College of Dublin, D04 V1W8 Dublin 4, Ireland)

  • Lucas Pereira

    (ITI, LARSyS, Técnico Lisboa, 1049-001 Lisboa, Portugal)

Abstract

The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

Suggested Citation

  • Benjamin Völker & Andreas Reinhardt & Anthony Faustine & Lucas Pereira, 2021. "Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective," Energies, MDPI, vol. 14(3), pages 1-21, January.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:719-:d:490126
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    References listed on IDEAS

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    Cited by:

    1. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    2. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    3. Andreas Reinhardt & Lucas Pereira, 2021. "Special Issue: “Energy Data Analytics for Smart Meter Data”," Energies, MDPI, vol. 14(17), pages 1-3, August.
    4. Yousaf Murtaza Rind & Muhammad Haseeb Raza & Muhammad Zubair & Muhammad Qasim Mehmood & Yehia Massoud, 2023. "Smart Energy Meters for Smart Grids, an Internet of Things Perspective," Energies, MDPI, vol. 16(4), pages 1-35, February.
    5. Jacopo Gaspari & Ernesto Antonini & Lia Marchi & Vincenzo Vodola, 2021. "Energy Transition at Home: A Survey on the Data and Practices That Lead to a Change in Household Energy Behavior," Sustainability, MDPI, vol. 13(9), pages 1-24, May.
    6. Filipe Quintal & Daniel Garigali & Dino Vasconcelos & Jonathan Cavaleiro & Wilson Santos & Lucas Pereira, 2021. "Energy Monitoring in the Wild: Platform Development and Lessons Learned from a Real-World Demonstrator," Energies, MDPI, vol. 14(18), pages 1-15, September.
    7. Serra, Daniele & Mardero, Daniele & Di Stefano, Luca & Grillo, Samuele, 2021. "Post-metering value-added services for low voltage electricity users: Lessons learned from the Italian experience of CHAIN 2," Applied Energy, Elsevier, vol. 304(C).

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