IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1974-d1070706.html
   My bibliography  Save this article

Smart Energy Meters for Smart Grids, an Internet of Things Perspective

Author

Listed:
  • Yousaf Murtaza Rind

    (Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Muhammad Haseeb Raza

    (Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Muhammad Zubair

    (Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Muhammad Qasim Mehmood

    (Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

  • Yehia Massoud

    (Innovative Technologies Laboratories (ITL), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia)

Abstract

Smart energy has evolved over the years to include multiple domains integrated across multiple technology themes, such as electricity, smart grid, and logistics, linked through communication technology and processed in the cloud in a holistic way to deliver on global challenges. Advances in sensing, communication, and computation technologies have been made that enable better smart system implementations. In smart energy systems, sensing technologies have spanned multiple domains with newer techniques that are more accurate, have greater dynamic ranges, and are more reliable. Similarly, communication techniques have now evolved into very high-speed, flexible, and dynamic systems. Computation techniques have seen a quantum leap with greater integration, powerful computing engines, and versatile software stacks that are easily available and modifiable. Finally, the system integration has also seen advances in the form of management, automation, and analytics paradigms. Consequently, smart energy systems have witnessed a revolutionary transformation. The complexity has correspondingly grown exponentially. With regard to smart meters, the measurement component has to scale up to meet the demands of the evolved energy eco-system by relying on the advancements offered. The internet of things (IoT) is a key technology enabler in this scenario, and the smart meter is a key component. In recent years, metering technology has evolved in both complexity and functionality. Therefore, it must use the advances offered by IoT to deliver a new role. The internet of things (IoT) is a key technology enabler in this scenario and the smart meter a key component. In recent years, metering technology has evolved in both complexity and functionality. To deliver on its new role, it must use the advances offered by IoT. In this review, we analyze the smart meter as a combination of sensing, computing, and communication nodes for flexible and complex design paradigms. The components are, in turn, reviewed vis-à-vis the advances offered by IoT. The resultant gaps are reported for future design challenges in the conclusion. The identified gaps are the lack of usage of the full spectrum of the available technology and the lack of an inter-disciplinary approach to smart meter design.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1974-:d:1070706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1974/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1974/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daisy Nkele Molokomme & Chabalala S. Chabalala & Pitshou N. Bokoro, 2020. "A Review of Cognitive Radio Smart Grid Communication Infrastructure Systems," Energies, MDPI, vol. 13(12), pages 1-20, June.
    2. Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
    3. Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
    4. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    5. van de Kaa, G. & Fens, T. & Rezaei, J. & Kaynak, D. & Hatun, Z. & Tsilimeni-Archangelidi, A., 2019. "Realizing smart meter connectivity: Analyzing the competing technologies Power line communication, mobile telephony, and radio frequency using the best worst method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 320-327.
    6. Razmjoo, Armin & Mirjalili, Seyedali & Aliehyaei, Mehdi & Østergaard, Poul Alberg & Ahmadi, Abolfazl & Majidi Nezhad, Meysam, 2022. "Development of smart energy systems for communities: technologies, policies and applications," Energy, Elsevier, vol. 248(C).
    7. Lund, Henrik & Østergaard, Poul Alberg & Connolly, David & Mathiesen, Brian Vad, 2017. "Smart energy and smart energy systems," Energy, Elsevier, vol. 137(C), pages 556-565.
    8. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
    9. Thellufsen, J.Z. & Lund, H. & Sorknæs, P. & Østergaard, P.A. & Chang, M. & Drysdale, D. & Nielsen, S. & Djørup, S.R. & Sperling, K., 2020. "Smart energy cities in a 100% renewable energy context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    10. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    11. Ali Hainoun & Hans-Martin Neumann & Naomi Morishita-Steffen & Baptiste Mougeot & Étienne Vignali & Florian Mandel & Felix Hörmann & Sebastian Stortecky & Katharina Walter & Martin Kaltenhauser-Barth &, 2022. "Smarter Together: Monitoring and Evaluation of Integrated Building Solutions for Low-Energy Districts of Lighthouse Cities Lyon, Munich, and Vienna," Energies, MDPI, vol. 15(19), pages 1-26, September.
    12. Al Khafaf, Nameer & Rezaei, Ahmad Asgharian & Moradi Amani, Ali & Jalili, Mahdi & McGrath, Brendan & Meegahapola, Lasantha & Vahidnia, Arash, 2022. "Impact of battery storage on residential energy consumption: An Australian case study based on smart meter data," Renewable Energy, Elsevier, vol. 182(C), pages 390-400.
    13. 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.
    14. Lilia Tightiz & Hyosik Yang, 2020. "A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication," Energies, MDPI, vol. 13(11), pages 1-24, June.
    15. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
    16. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
    17. Lund, Henrik & Thellufsen, Jakob Zinck & Sorknæs, Peter & Mathiesen, Brian Vad & Chang, Miguel & Madsen, Poul Thøis & Kany, Mikkel Strunge & Skov, Iva Ridjan, 2022. "Smart energy Denmark. A consistent and detailed strategy for a fully decarbonized society," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    18. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    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. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
    2. Wei Li & Jiekai Shi & Hanyun Zhou, 2024. "Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid," Energies, MDPI, vol. 17(13), pages 1-17, June.
    3. Giovanni Artale & Nicola Panzavecchia & Valentina Cosentino & Antonio Cataliotti & Manel Ben-Romdhane & Amel Benazza-Ben Yahia & Valeria Boscaino & Noureddine Ben Othman & Vito Ditta & Michele Fiorino, 2023. "CZT-Based Harmonic Analysis in Smart Grid Using Low-Cost Electronic Measurement Boards," Energies, MDPI, vol. 16(10), pages 1-25, May.
    4. Marco Bindi & Maria Cristina Piccirilli & Antonio Luchetta & Francesco Grasso, 2023. "A Comprehensive Review of Fault Diagnosis and Prognosis Techniques in High Voltage and Medium Voltage Electrical Power Lines," Energies, MDPI, vol. 16(21), pages 1-37, October.

    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. Sorknæs, Peter & Thellufsen, Jakob Zinck & Knobloch, Kai & Engelbrecht, Kurt & Yuan, Meng, 2023. "Economic potentials of carnot batteries in 100% renewable energy systems," Energy, Elsevier, vol. 282(C).
    2. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Zhou, Jing & Zhang, Li & Fan, Lin & Yang, Zhaoming & Xie, Fei & Zuo, Lili & Zhang, Jinjun, 2023. "A systematic framework for the assessment of the reliability of energy supply in Integrated Energy Systems based on a quasi-steady-state model," Energy, Elsevier, vol. 263(PB).
    3. Osorio-Aravena, Juan Carlos & Aghahosseini, Arman & Bogdanov, Dmitrii & Caldera, Upeksha & Ghorbani, Narges & Mensah, Theophilus Nii Odai & Haas, Jannik & Muñoz-Cerón, Emilio & Breyer, Christian, 2023. "Synergies of electrical and sectoral integration: Analysing geographical multi-node scenarios with sector coupling variations for a transition towards a fully renewables-based energy system," Energy, Elsevier, vol. 279(C).
    4. Wang, Jiangjiang & Deng, Hongda & Qi, Xiaoling, 2022. "Cost-based site and capacity optimization of multi-energy storage system in the regional integrated energy networks," Energy, Elsevier, vol. 261(PA).
    5. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Lund, Henrik & Thellufsen, Jakob Zinck & Sorknæs, Peter & Mathiesen, Brian Vad & Chang, Miguel & Madsen, Poul Thøis & Kany, Mikkel Strunge & Skov, Iva Ridjan, 2022. "Smart energy Denmark. A consistent and detailed strategy for a fully decarbonized society," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. Flores, Francisco & Feijoo, Felipe & DeStephano, Paelina & Herc, Luka & Pfeifer, Antun & Duić, Neven, 2024. "Assessment of the impacts of renewable energy variability in long-term decarbonization strategies," Applied Energy, Elsevier, vol. 368(C).
    8. Yuan, Meng & Vad Mathiesen, Brian & Schneider, Noémi & Xia, Jianjun & Zheng, Wen & Sorknæs, Peter & Lund, Henrik & Zhang, Lipeng, 2024. "Renewable energy and waste heat recovery in district heating systems in China: A systematic review," Energy, Elsevier, vol. 294(C).
    9. Pastore, Lorenzo Mario & Lo Basso, Gianluigi & Sforzini, Matteo & de Santoli, Livio, 2022. "Technical, economic and environmental issues related to electrolysers capacity targets according to the Italian Hydrogen Strategy: A critical analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    10. Hussain, Syed Asad & Razi, Faran & Hewage, Kasun & Sadiq, Rehan, 2023. "The perspective of energy poverty and 1st energy crisis of green transition," Energy, Elsevier, vol. 275(C).
    11. Mimica, Marko & Krajačić, Goran, 2021. "The Smart Islands method for defining energy planning scenarios on islands," Energy, Elsevier, vol. 237(C).
    12. Simone Ferrari & Milad Zoghi & Giancarlo Paganin & Giuliano Dall’O’, 2023. "A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock," Energies, MDPI, vol. 16(15), pages 1-35, July.
    13. Alexander Tureczek & Per Sieverts Nielsen & Henrik Madsen, 2018. "Electricity Consumption Clustering Using Smart Meter Data," Energies, MDPI, vol. 11(4), pages 1-18, April.
    14. Rasmus Magni Johannsen & Poul Alberg Østergaard & David Maya-Drysdale & Louise Krog Elmegaard Mouritsen, 2021. "Designing Tools for Energy System Scenario Making in Municipal Energy Planning," Energies, MDPI, vol. 14(5), pages 1-17, March.
    15. 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.
    16. Mittelman, Gur & Eran, Ronen & Zhivin, Lev & Eisenhändler, Ohad & Luzon, Yossi & Tshuva, Moshe, 2023. "The potential of renewable electricity in isolated grids: The case of Israel in 2050," Applied Energy, Elsevier, vol. 349(C).
    17. Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
    18. Eunsung Oh & Sung-Yong Son, 2022. "Appropriate Technology-Based AMI Deployment in Multi-Dwelling Units," Energies, MDPI, vol. 15(4), pages 1-12, February.
    19. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    20. Kılkış, Şiir, 2022. "Urban emissions and land use efficiency scenarios towards effective climate mitigation in urban systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

    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:16:y:2023:i:4:p:1974-:d:1070706. 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.