IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p8171-d695957.html
   My bibliography  Save this article

A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids

Author

Listed:
  • Asfandyar Khan

    (Department of Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan)

  • Arif Iqbal Umar

    (Department of Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan)

  • Arslan Munir

    (Intelligent Systems, Computer Architecture, Analytics, and Security (ISCAAS) Laboratory, Department of Computer Science, Kansas State University, Manhattan, KA 66506, USA)

  • Syed Hamad Shirazi

    (Department of Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan)

  • Muazzam A. Khan

    (Department of Computer Science, Quid-i-Azam University, Islamabad 44000, Pakistan)

  • Muhammad Adnan

    (Department of Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan)

Abstract

The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

Suggested Citation

  • Asfandyar Khan & Arif Iqbal Umar & Arslan Munir & Syed Hamad Shirazi & Muazzam A. Khan & Muhammad Adnan, 2021. "A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids," Energies, MDPI, vol. 14(23), pages 1-22, December.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8171-:d:695957
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/8171/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/23/8171/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arghira, Nicoleta & Hawarah, Lamis & Ploix, Stéphane & Jacomino, Mireille, 2012. "Prediction of appliances energy use in smart homes," Energy, Elsevier, vol. 48(1), pages 128-134.
    2. 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.
    3. Kabalci, Yasin, 2016. "A survey on smart metering and smart grid communication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 302-318.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    3. Nguyen, Hieu Trung & Battula, Swathi & Takkala, Rohit Reddy & Wang, Zhaoyu & Tesfatsion, Leigh S., 2018. "Transactive Energy Design for Integrated Transmission and Distribution Systems," ISU General Staff Papers 201802280800001000, Iowa State University, Department of Economics.
    4. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    5. Muratori, Matteo & Roberts, Matthew C. & Sioshansi, Ramteen & Marano, Vincenzo & Rizzoni, Giorgio, 2013. "A highly resolved modeling technique to simulate residential power demand," Applied Energy, Elsevier, vol. 107(C), pages 465-473.
    6. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    7. Kolasa, Piotr & Janowski, Mirosław, 2017. "Study of possibilities to store energy virtually in a grid (VESS) with the use of smart metering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1513-1517.
    8. Giovanni Artale & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Riccardo Fiorelli & Salvatore Guaiana & Nicola Panzavecchia & Giovanni Tinè, 2019. "A New Coupling Solution for G3-PLC Employment in MV Smart Grids," Energies, MDPI, vol. 12(13), pages 1-23, June.
    9. Hussain, Shahbaz & Hernandez Fernandez, Javier & Al-Ali, Abdulla Khalid & Shikfa, Abdullatif, 2021. "Vulnerabilities and countermeasures in electrical substations," International Journal of Critical Infrastructure Protection, Elsevier, vol. 33(C).
    10. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    11. Liu, Xiufeng & Nielsen, Per Sieverts, 2016. "A hybrid ICT-solution for smart meter data analytics," Energy, Elsevier, vol. 115(P3), pages 1710-1722.
    12. 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.
    13. Hou, Langbo & Tong, Xi & Chen, Heng & Fan, Lanxin & Liu, Tao & Liu, Wenyi & Liu, Tong, 2024. "Optimized scheduling of smart community energy systems considering demand response and shared energy storage," Energy, Elsevier, vol. 295(C).
    14. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    15. Ray, Manojit & Chakraborty, Basab, 2019. "Impact of evolving technology on collaborative energy access scaling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 13-27.
    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. Siewierski, Tomasz & Szypowski, Michał & Wędzik, Andrzej, 2018. "A review of economic aspects of voltage control in LV smart grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 37-45.
    18. Palacios-Garcia, E.J. & Moreno-Munoz, A. & Santiago, I. & Flores-Arias, J.M. & Bellido-Outeirino, F.J. & Moreno-Garcia, I.M., 2018. "A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector," Energy, Elsevier, vol. 144(C), pages 1080-1091.
    19. Omowunmi Mary Longe & Khmaies Ouahada, 2018. "Mitigating Household Energy Poverty through Energy Expenditure Affordability Algorithm in a Smart Grid," Energies, MDPI, vol. 11(4), pages 1-17, April.
    20. Antonio E. Saldaña-González & Andreas Sumper & Mònica Aragüés-Peñalba & Miha Smolnikar, 2020. "Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review," Energies, MDPI, vol. 13(14), pages 1-34, July.

    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:23:p:8171-:d:695957. 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.