IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i3p719-d490126.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/3/719/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/3/719/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sara Fontdecaba & José Sánchez-Espigares & Lluís Marco-Almagro & Xavier Tort-Martorell & Francesc Cabrespina & Jordi Zubelzu, 2013. "An Approach to Disaggregating Total Household Water Consumption into Major End-Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2155-2177, May.
    2. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    3. Cabeza, Luisa F. & Ürge-Vorsatz, Diana & Palacios, Anabel & Ürge, Daniel & Serrano, Susana & Barreneche, Camila, 2018. "Trends in penetration and ownership of household appliances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4044-4059.
    4. Raneen Younis & Andreas Reinhardt, 2020. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures," Energies, MDPI, vol. 13(12), pages 1-19, June.
    5. Paula Meehan & Conor McArdle & Stephen Daniels, 2014. "An Efficient, Scalable Time-Frequency Method for Tracking Energy Usage of Domestic Appliances Using a Two-Step Classification Algorithm," Energies, MDPI, vol. 7(11), pages 1-26, October.
    6. Jamasb,Tooraj & Pollitt,Michael G. (ed.), 2011. "The Future of Electricity Demand," Cambridge Books, Cambridge University Press, number 9781107008502, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
    2. Hasan Rafiq & Xiaohan Shi & Hengxu Zhang & Huimin Li & Manesh Kumar Ochani, 2020. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing," Energies, MDPI, vol. 13(9), pages 1-26, May.
    3. Jana Huchtkoetter & Marcel Alwin Tepe & Andreas Reinhardt, 2021. "The Impact of Ambient Sensing on the Recognition of Electrical Appliances," Energies, MDPI, vol. 14(1), pages 1-23, January.
    4. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    5. Katarzyna Stasiuk & Dominika Maison, 2022. "The Influence of New and Old Energy Labels on Consumer Judgements and Decisions about Household Appliances," Energies, MDPI, vol. 15(4), pages 1-13, February.
    6. Chatzigeorgiou, I.M. & Andreou, G.T., 2021. "A systematic review on feedback research for residential energy behavior change through mobile and web interfaces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    7. Claire M. Weiller & Michael G. Pollitt, 2013. "Platform markets and energy services," Working Papers EPRG 1334, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    8. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    9. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    10. Lang, Corey & Okwelum, Edson, 2015. "The mitigating effect of strategic behavior on the net benefits of a direct load control program," Energy Economics, Elsevier, vol. 49(C), pages 141-148.
    11. Coelho, Igor M. & Coelho, Vitor N. & Luz, Eduardo J. da S. & Ochi, Luiz S. & Guimarães, Frederico G. & Rios, Eyder, 2017. "A GPU deep learning metaheuristic based model for time series forecasting," Applied Energy, Elsevier, vol. 201(C), pages 412-418.
    12. Iana Vassileva & Javier Campillo, 2016. "Consumers’ Perspective on Full-Scale Adoption of Smart Meters: A Case Study in Västerås, Sweden," Resources, MDPI, vol. 5(1), pages 1-18, January.
    13. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    14. Benjamin Völker & Marc Pfeifer & Philipp M. Scholl & Bernd Becker, 2020. "A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(1), pages 1-26, December.
    15. Brophy Haney, A. & Jamasb, T. & Platchkov, L.M. & Pollitt, M.G., 2010. "Demand-side Management Strategies and the Residential Sector: Lessons from International Experience," Cambridge Working Papers in Economics 1060, Faculty of Economics, University of Cambridge.
    16. Astier, Nicolas, 2018. "Comparative feedbacks under incomplete information," Resource and Energy Economics, Elsevier, vol. 54(C), pages 90-108.
    17. Cristina Puente & Rafael Palacios & Yolanda González-Arechavala & Eugenio Francisco Sánchez-Úbeda, 2020. "Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques," Energies, MDPI, vol. 13(12), pages 1-20, June.
    18. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    19. Pérez-Sánchez, Laura À. & Velasco-Fernández, Raúl & Giampietro, Mario, 2022. "Factors and actions for the sustainability of the residential sector. The nexus of energy, materials, space, and time use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    20. Walker, Sara Louise & Hope, Alex & Bentley, Edward, 2014. "Modelling steady state performance of a local electricity distribution system under UK 2050 carbon pathway scenarios," Energy, Elsevier, vol. 78(C), pages 604-621.

    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:gam:jeners:v:14:y:2021:i:3:p:719-:d:490126. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.