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Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review

Author

Listed:
  • Joy Dalmacio Billanes

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, The Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark)

  • Zheng Grace Ma

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, The Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark)

  • Bo Nørregaard Jørgensen

    (SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, The Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark)

Abstract

Data-driven technologies in smart buildings offer significant opportunities to enhance energy efficiency, sustainability, and occupant comfort. However, the existing literature often lacks a holistic examination of the technological advancements, adoption barriers, and business models necessary to realize these benefits. To address this gap, this scoping review synthesizes current research on these technologies, identifies factors influencing their adoption, and examines supporting business models. Inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured search of the literature across four major databases yielded 112 relevant studies. The key technologies identified included big data analytics, Artificial Intelligence, Machine Learning, the Internet of Things, Wireless Sensor Networks, Edge and Cloud Computing, Blockchain, Digital Twins, and Geographic Information Systems. Energy optimization is further achieved through integrating renewable energy resources and advanced energy management systems, such as Home Energy Management Systems and Building Energy Management Systems. Factors influencing adoption are categorized into social influences, individual perceptions, cost considerations, security and privacy concerns, and data quality issues. The analysis of business models emphasizes the need to align technological innovations with market needs, focusing on value propositions like cost savings and efficiency improvements. Despite the benefits, challenges such as high initial costs, technical complexities, security risks, and user acceptance hinder their widespread adoption. This review highlights the importance of addressing these challenges through the development of cost-effective, interoperable, secure, and user-centric solutions, offering a roadmap for future research and industry applications.

Suggested Citation

  • Joy Dalmacio Billanes & Zheng Grace Ma & Bo Nørregaard Jørgensen, 2025. "Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review," Energies, MDPI, vol. 18(2), pages 1-49, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:290-:d:1564423
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    References listed on IDEAS

    as
    1. Katarina Bäcklund & Marco Molinari & Per Lundqvist & Björn Palm, 2023. "Building Occupants, Their Behavior and the Resulting Impact on Energy Use in Campus Buildings: A Literature Review with Focus on Smart Building Systems," Energies, MDPI, vol. 16(17), pages 1-21, August.
    2. Elnour, Mariam & Fadli, Fodil & Himeur, Yassine & Petri, Ioan & Rezgui, Yacine & Meskin, Nader & Ahmad, Ahmad M., 2022. "Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    3. Prativa Lamsal & Sushil Bahadur Bajracharya & Hom Bahadur Rijal, 2023. "A Review on Adaptive Thermal Comfort of Office Building for Energy-Saving Building Design," Energies, MDPI, vol. 16(3), pages 1-23, February.
    4. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review," Energies, MDPI, vol. 16(6), pages 1-23, March.
    5. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
    6. Le, Duc Nha & Le Tuan, Loc & Dang Tuan, Minh Nguyen, 2019. "Smart-building management system: An Internet-of-Things (IoT) application business model in Vietnam," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 22-35.
    7. Bianca Gasparetto Rebelatto & Amanda Lange Salvia & Luciana Londero Brandli & Walter Leal Filho, 2024. "Examining Energy Efficiency Practices in Office Buildings through the Lens of LEED, BREEAM, and DGNB Certifications," Sustainability, MDPI, vol. 16(11), pages 1-24, May.
    8. Adrian Micu & Angela-Eliza Micu & Marius Geru & Alexandru Capatina & Mihaela-Carmen Muntean, 2021. "The Challenge for Energy Saving in Smart Homes: Exploring the Interest for IoT Devices Acquisition in Romania," Energies, MDPI, vol. 14(22), pages 1-12, November.
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