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A Survey on Energy Efficiency in Smart Homes and Smart Grids

Author

Listed:
  • Lisardo Prieto González

    (Software Architect Group, Department of Computer Science, Carlos III University of Madrid, 28911 Leganés, Spain)

  • Anna Fensel

    (Wageningen Data Competence Center (WDCC), Wageningen University and Research, 6708 PB Wageningen, The Netherlands
    Consumption & Healthy Lifestyles Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
    STI (Semantic Technology Institute) Innsbruck, Department of Computer Science, University of Innsbruck, 6020 Innsbruck, Austria)

  • Juan Miguel Gómez Berbís

    (Software Architect Group, Department of Computer Science, Carlos III University of Madrid, 28911 Leganés, Spain)

  • Angela Popa

    (STI (Semantic Technology Institute) Innsbruck, Department of Computer Science, University of Innsbruck, 6020 Innsbruck, Austria)

  • Antonio de Amescua Seco

    (Software Architect Group, Department of Computer Science, Carlos III University of Madrid, 28911 Leganés, Spain)

Abstract

Empowered by the emergence of novel information and communication technologies (ICTs) such as sensors and high-performance digital communication systems, Europe has adapted its electricity distribution network into a modern infrastructure known as a smart grid (SG). The benefits of this new infrastructure include precise and real-time capacity for measuring and monitoring the different energy-relevant parameters on the various points of the grid and for the remote operation and optimization of distribution. Furthermore, a new user profile is derived from this novel infrastructure, known as a prosumer (a user that can produce and consume energy to/from the grid), who can benefit from the features derived from applying advanced analytics and semantic technologies in the rich amount of big data generated by the different subsystems. However, this novel, highly interconnected infrastructure also presents some significant drawbacks, like those related to information security (IS). We provide a systematic literature survey of the ICT-empowered environments that comprise SGs and homes, and the application of modern artificial intelligence (AI) related technologies with sensor fusion systems and actuators, ensuring energy efficiency in such systems. Furthermore, we outline the current challenges and outlook for this field. These address new developments on microgrids, and data-driven energy efficiency that leads to better knowledge representation and decision-making for smart homes and SGs.

Suggested Citation

  • Lisardo Prieto González & Anna Fensel & Juan Miguel Gómez Berbís & Angela Popa & Antonio de Amescua Seco, 2021. "A Survey on Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(21), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7273-:d:671587
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    References listed on IDEAS

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    1. Claudia Dobler & Dominik Pfeifer & Wolfgang Streicher, 2018. "Reaching energy autonomy in a medium‐sized city – three scenarios to model possible future energy developments in the residential building sector," Sustainable Development, John Wiley & Sons, Ltd., vol. 26(6), pages 859-869, November.
    2. Marikyan, Davit & Papagiannidis, Savvas & Alamanos, Eleftherios, 2019. "A systematic review of the smart home literature: A user perspective," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 139-154.
    3. Zhang, Hongwei & Wang, Jinsong & Ding, Yuemin, 2019. "Blockchain-based decentralized and secure keyless signature scheme for smart grid," Energy, Elsevier, vol. 180(C), pages 955-967.
    4. Anna Fensel & Juan Miguel Gómez Berbís, 2021. "Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(8), pages 1-2, April.
    5. Josue Campos do Prado & Wei Qiao & Liyan Qu & Julio Romero Agüero, 2019. "The Next-Generation Retail Electricity Market in the Context of Distributed Energy Resources: Vision and Integrating Framework," Energies, MDPI, vol. 12(3), pages 1-24, February.
    6. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
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    Citations

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    Cited by:

    1. Jacek Strojny & Anna Krakowiak-Bal & Jarosław Knaga & Piotr Kacorzyk, 2023. "Energy Security: A Conceptual Overview," Energies, MDPI, vol. 16(13), pages 1-35, June.
    2. Vasileios M. Laitsos & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2021. "An Incentive-Based Implementation of Demand Side Management in Power Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    3. Wadim Strielkowski & Olga Kovaleva & Tatiana Efimtseva, 2022. "Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    4. José F. C. Castro & Ronaldo A. Roncolatto & Antonio R. Donadon & Vittoria E. M. S. Andrade & Pedro Rosas & Rafael G. Bento & José G. Matos & Fernando A. Assis & Francisco C. R. Coelho & Rodolfo Quadro, 2023. "Microgrid Applications and Technical Challenges—The Brazilian Status of Connection Standards and Operational Procedures," Energies, MDPI, vol. 16(6), pages 1-25, March.

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