IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2024i1p58-d1553259.html

Data-Driven Ventilation and Energy Optimization in Smart Office Buildings: Insights from a High-Resolution Occupancy and Indoor Climate Dataset

Author

Listed:
  • Haidar Hosamo

    (The SALab Sustainable Architecture Lab, Department of Architecture, Prince Sultan University, Riyadh 12435, Saudi Arabia
    The Department of Built Environment, Oslo Metropolitan University, St. Olavs Plass, P.O. Box 4, 0130 Oslo, Norway)

  • Silvia Mazzetto

    (The SALab Sustainable Architecture Lab, Department of Architecture, Prince Sultan University, Riyadh 12435, Saudi Arabia)

Abstract

This paper explores innovative approaches to reducing energy consumption in building ventilation systems through the implementation of adaptive control strategies. Using a publicly available high-resolution dataset spanning a full year, the study integrates real-time data on occupancy, CO 2 levels, temperature, window state, and external environmental conditions. Notably, occupancy data derived from computer vision-based detection using the YOLOv5 algorithm provides an unprecedented level of granularity. The study evaluates five energy-saving strategies: Demand-Controlled Ventilation (DCV), occupancy-based control, time-based off-peak reduction, window-open control, and temperature-based control. Among these, the occupancy-based strategy achieved the highest energy savings, reducing power consumption by 50%, while temperature-based control yielded a significant 37.27% reduction. This paper’s originality lies in its holistic analysis of multiple dynamic control strategies, integrating diverse environmental and operational variables rarely combined in prior research. The findings highlight the transformative potential of integrating real-time environmental data and advanced control algorithms to optimize HVAC performance. This study establishes a new benchmark for energy-efficient building management through offering practical recommendations and laying the groundwork for predictive models, renewable energy integration, and occupant-centric systems.

Suggested Citation

  • Haidar Hosamo & Silvia Mazzetto, 2024. "Data-Driven Ventilation and Energy Optimization in Smart Office Buildings: Insights from a High-Resolution Occupancy and Indoor Climate Dataset," Sustainability, MDPI, vol. 17(1), pages 1-36, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:58-:d:1553259
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/1/58/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/1/58/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard Nagy & Ľudmila Mečiarová & Silvia Vilčeková & Eva Krídlová Burdová & Danica Košičanová, 2019. "Investigation of a Ventilation System for Energy Efficiency and Indoor Environmental Quality in a Renovated Historical Building: A Case Study," IJERPH, MDPI, vol. 16(21), pages 1-17, October.
    2. Kong, Meng & Dong, Bing & Zhang, Rongpeng & O'Neill, Zheng, 2022. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study," Applied Energy, Elsevier, vol. 306(PA).
    3. Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
    4. Naylor, Sophie & Gillott, Mark & Lau, Tom, 2018. "A review of occupant-centric building control strategies to reduce building energy use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 1-10.
    5. Heidari, Amirreza & Maréchal, François & Khovalyg, Dolaana, 2022. "An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 312(C).
    6. Silvia Mazzetto, 2024. "A Review of Urban Digital Twins Integration, Challenges, and Future Directions in Smart City Development," Sustainability, MDPI, vol. 16(19), pages 1-33, September.
    7. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    8. Kolthoom Alkofahi & Ivan A. Duran & Najia Saqib, 2024. "Net-Zero Emissions Pathways in BRICS Economies: The Impact of Environmental Innovations, Policy, and Human Capital on Carbon Footprint Reduction," International Journal of Energy Economics and Policy, Econjournals, vol. 14(6), pages 106-113, November.
    9. Ivan A. Duran & Najia Saqib, 2024. "Load Capacity Factor and Environmental Quality: Unveiling the Role of Economic Growth, Green Innovations, and Environmental Policies in G20 Economies," International Journal of Energy Economics and Policy, Econjournals, vol. 14(6), pages 287-294, November.
    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. Mahmud, Arafat & Dhrubo, Ehsan Ahmed & Ahmed, S. Shahnawaz & Chowdhury, Abdul Hasib & Hossain, Md. Farhad & Rahman, Hamidur & Masood, Nahid-Al, 2022. "Energy conservation for existing cooling and lighting loads," Energy, Elsevier, vol. 255(C).
    2. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Pang, Zhihong & Chen, Yan & Zhang, Jian & O'Neill, Zheng & Cheng, Hwakong & Dong, Bing, 2020. "Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates," Applied Energy, Elsevier, vol. 279(C).
    4. Heidari, Amirreza & Girardin, Luc & Dorsaz, Cédric & Maréchal, François, 2025. "A trustworthy reinforcement learning framework for autonomous control of a large-scale complex heating system: Simulation and field implementation," Applied Energy, Elsevier, vol. 378(PA).
    5. Guanjing Lin & Armando Casillas & Maggie Sheng & Jessica Granderson, 2023. "Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building," Energies, MDPI, vol. 16(20), pages 1-21, October.
    6. Jiang, Zixin & Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk," Applied Energy, Elsevier, vol. 334(C).
    7. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    8. 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.
    9. John Kaiser Calautit & Hassam Nasarullah Chaudhry, 2022. "Sustainable Buildings: Heating, Ventilation, and Air-Conditioning," Energies, MDPI, vol. 15(21), pages 1-5, November.
    10. Walaa Mohamed Metwally, 2025. "Biomimicry and Green Architecture: Nature-Inspired Innovations for Sustainable Buildings," Sustainability, MDPI, vol. 17(16), pages 1-19, August.
    11. Imran & Naeem Iqbal & Shabir Ahmad & Do Hyeun Kim, 2021. "Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    12. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    13. Liao, Chenxin & Miyata, Shohei & Qu, Ming & Akashi, Yasunori, 2025. "Year-round operational optimization of HVAC systems using hierarchical deep reinforcement learning for enhancing indoor air quality and reducing energy consumption," Applied Energy, Elsevier, vol. 390(C).
    14. Wenbo Zhao & Ling Fan, 2024. "Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model," Sustainability, MDPI, vol. 16(6), pages 1-21, March.
    15. Guo, Siyi & Wei, Ziqing & Wu, Huiying & Fang, Haizhou & Zhai, Xiaoqiang, 2025. "Real-time prediction of thermal load and indoor temperature for buildings with a recursive identification method in noise conditions," Energy, Elsevier, vol. 335(C).
    16. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    17. Jacobs, Stef & Ghane, Sara & Houben, Pieter Jan & Kabbara, Zakarya & Huybrechts, Thomas & Hellinckx, Peter & Verhaert, Ivan, 2025. "Improving the learning process of deep reinforcement learning agents operating in collective heating environments," Applied Energy, Elsevier, vol. 384(C).
    18. Wang, Xuezheng & Dong, Bing, 2024. "Long-term experimental evaluation and comparison of advanced controls for HVAC systems," Applied Energy, Elsevier, vol. 371(C).
    19. Hall, Rebecca & Kenway, Steven & O'Brien, Katherine & Memon, Fayyaz, 2025. "Quantification of residential water-related energy needs cohesion, validation and global representation to unlock efficiency gains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    20. Chen, Yuhui & Du, Longzheng & Zhang, Biao & Wang, Lin & Wang, Kejuan & Huang, Xuebin & Shi, Yujie, 2025. "The impact of artificial intelligence on the sustainability of international trade enterprises," International Review of Economics & Finance, Elsevier, vol. 101(C).

    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:jsusta:v:17:y:2024:i:1:p:58-:d:1553259. 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.