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Occupancy-Based Predictive AI-Driven Ventilation Control for Energy Savings in Office Buildings

Author

Listed:
  • Violeta Motuzienė

    (Department of Building Energetics, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, 10223 Vilnius, Lithuania)

  • Jonas Bielskus

    (Department of Building Energetics, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, 10223 Vilnius, Lithuania)

  • Rasa Džiugaitė-Tumėnienė

    (Department of Building Energetics, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, 10223 Vilnius, Lithuania)

  • Vidas Raudonis

    (Automation Department, Kaunas University of Technology, K. Donelaičio St. 73, 44249 Kaunas, Lithuania)

Abstract

Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO 2 concentrations below 1000 ppm, and up to 12.5% if CO 2 concentrations are slightly above 1000 ppm for 1.5% of the time.

Suggested Citation

  • Violeta Motuzienė & Jonas Bielskus & Rasa Džiugaitė-Tumėnienė & Vidas Raudonis, 2025. "Occupancy-Based Predictive AI-Driven Ventilation Control for Energy Savings in Office Buildings," Sustainability, MDPI, vol. 17(9), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4140-:d:1648703
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    References listed on IDEAS

    as
    1. Violeta Motuzienė & Vilūnė Lapinskienė & Genrika Rynkun, 2024. "Optimizing Ventilation Systems for Sustainable Office Buildings: Long-Term Monitoring and Environmental Impact Analysis," Sustainability, MDPI, vol. 16(3), pages 1-16, January.
    2. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    3. Wang, Hao & Chen, Xiwen & Vital, Natan & Duffy, Edward & Razi, Abolfazl, 2024. "Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 356(C).
    4. Shi, Shanrui & Miyata, Shohei & Akashi, Yasunori, 2025. "Event-driven model-based optimal demand-controlled ventilation for multizone VAV systems: Enhancing energy efficiency and indoor environmental quality," Applied Energy, Elsevier, vol. 377(PD).
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