IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p290-d1564423.html

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/290/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/2/290/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Zhou, Ying & Wang, Yu & Li, Chenshuang & Ding, Lieyun & Yang, Zhigang, 2024. "Energy-efficiency oriented occupancy space optimization in buildings: A data-driven approach based on multi-sensor fusion considering behavior-environment integration," Energy, Elsevier, vol. 299(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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).
    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. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Elias Tsirovasilis & Martha Katafygiotou & Chrystala Psathiti, 2025. "Comparative Assessment of LEED, BREEAM, and WELL: Advancing Sustainable Built Environments," Energies, MDPI, vol. 18(16), pages 1-23, August.
    3. Tsai, I-Chun, 2024. "A wise investment by urban governments: Evidence from intelligent sports facilities," Journal of Asian Economics, Elsevier, vol. 92(C).
    4. Abdul Hayy Haziq Mohamad & Abdul Rahman Edruce Kushairi & James Ho Chun Bui, 2025. "Socioeconomic and Locational Determinants of House Buying Behaviour in Bintulu: A Micro-Level Urban Housing Analysis," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(8), pages 886-895, August.
    5. Tirulo, Aschalew & Yadav, Monika & Lolamo, Mathewos & Chauhan, Siddhartha & Siano, Pierluigi & Shafie-khah, Miadreza, 2026. "Beyond automation: Unveiling the potential of agentic intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PA).
    6. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments," Sustainability, MDPI, vol. 15(18), pages 1-27, September.
    7. Hua Chen & Shuang Dai & Fanlin Meng, 2023. "Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    8. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumarai Chelvan & Hossen Teimoorinia, 2025. "Occupant-Centric Load Optimization in Smart Green Townhouses Using Machine Learning," Energies, MDPI, vol. 18(13), pages 1-15, June.
    9. Rocha, Helder R.O. & Fiorotti, Rodrigo & Louzada, Danilo M. & Silvestre, Leonardo J. & Celeste, Wanderley C. & Silva, Jair A.L., 2024. "Net Zero Energy cost Building system design based on Artificial Intelligence," Applied Energy, Elsevier, vol. 355(C).
    10. Seddigheh Norouziasl & Sorena Vosoughkhosravi & Amirhosein Jafari & Zhihong Pang, 2024. "Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings," Energies, MDPI, vol. 17(21), pages 1-31, October.
    11. Cai, Helen & Zhang, Wanhao & Yuan, Qiong & Salameh, Anas A. & Alahmari, Saad & Ferrara, Massimiliano, 2025. "Cost-effective intelligent building: Energy management system using machine learning and multi-criteria decision support," Energy Economics, Elsevier, vol. 142(C).
    12. Sayed, Aya Nabil & Himeur, Yassine & Varlamis, Iraklis & Bensaali, Faycal, 2025. "Continual learning for energy management systems: A review of methods and applications, and a case study," Applied Energy, Elsevier, vol. 384(C).
    13. Du, Han & Zhou, Xinlei & Nord, Natasa & Carden, Yale & Cui, Ping & Ma, Zhenjun, 2025. "Development of a robust data-driven surrogate model to improve energy flexibility of an integrated district heating system with a thermal storage system," Energy, Elsevier, vol. 336(C).
    14. Amjad Almusaed & Asaad Almssad & Asaad Alasadi & Ibrahim Yitmen & Sammera Al-Samaraee, 2023. "Assessing the Role and Efficiency of Thermal Insulation by the “BIO-GREEN PANEL” in Enhancing Sustainability in a Built Environment," Sustainability, MDPI, vol. 15(13), pages 1-25, July.
    15. Aidil Syukri Shaari & Mohd Sofian Rosbi & Ernie Che Mid, 2024. "Pilot Study Analysis for Energy Conservation Behaviour Using Value-Belief-Norm (VBN) Theory in Malaysian Universities Perspective," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(14), pages 505-517, November.
    16. Mubarak, Muhammad Faraz & Petraite, Monika, 2020. "Industry 4.0 technologies, digital trust and technological orientation: What matters in open innovation?," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    17. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin & Tai, Nengling, 2025. "Objective-driven charging control and learning-based incentive pricing for demand response of electric vehicles," Energy, Elsevier, vol. 338(C).
    18. Zhang, Limao & Guo, Jing & Lin, Penghui & Tiong, Robert L.K., 2025. "Detecting energy consumption anomalies with dynamic adaptive encoder-decoder deep learning networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    19. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumarai Chelvan & Hossen Teimoorinia, 2024. "Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode," Energies, MDPI, vol. 17(24), pages 1-25, December.
    20. Kahori Genjo & Haruna Nakanishi & Momoka Oki & Hikaru Imagawa & Tomoko Uno & Teruyuki Saito & Hiroshi Takata & Kazuyo Tsuzuki & Takashi Nakaya & Daisaku Nishina & Kenichi Hasegawa & Taro Mori & Hom Ba, 2023. "Development of Adaptive Model and Occupant Behavior Model in Four Office Buildings in Nagasaki, Japan," Energies, MDPI, vol. 16(16), pages 1-30, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:2:p:290-:d:1564423. 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.