EVALUATING THE EFFICIENCY OF HVAC SYSTEMS CONSIDERING OCCUPANCY AND CO₂ CONCENTRATION
Abstract
This study examines the effectiveness of HVAC (Heating, Ventilation, and Air Conditioning) systems that integrate occupancy and CO₂ sensing technologies. These smart systems enable real-time, demand-controlled ventilation (DCV), dynamically adjusting airflow to maintain indoor air quality while reducing energy use. The article presents a comprehensive analysis of modern sensor technologies, mathematical models, and AI-based control methods such as Model Predictive Control (MPC) and Reinforcement Learning (RL). It also compares international ventilation standards and outlines key implementation challenges. Findings show that integrating occupancy and CO₂ data into HVAC control can yield energy savings of 10–30% while improving indoor air quality and cognitive performance. The results support the transition toward intelligent, energy-efficient, and health-conscious building environments.
References
1. IEA. Energy Efficiency 2023. International Energy Agency, 2023.
2. Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398.
3. Wang, S., & Ma, Z. (2008). Supervisory and optimal control of building HVAC systems: A review. HVAC&R Research, 14(1), 3–32.
4. Satish, U., et al. (2012). Is CO₂ an Indoor Pollutant? Direct Effects of Low-to-Moderate CO₂ Concentrations on Human Decision-Making Performance. Environmental Health Perspectives, 120(12), 1671–1677.
5. Allen, J. G., et al. (2016). Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation, and Volatile Organic Compound Exposures in Office Workers. Environmental Health Perspectives, 124(6), 805–812.
6. Afram, A., & Janabi-Sharifi, F. (2014). Theory and applications of HVAC control systems–A review of model predictive control (MPC). Building and Environment, 72, 343–355.
7. Vázquez-Canteli, J. R., & Nagy, Z. (2019). Reinforcement learning for demand response: A review of algorithms and modeling techniques. Applied Energy, 235, 1072–1089.
8. Lu, T., & Viljanen, M. (2010). Demand-controlled ventilation based on occupancy and indoor air quality. Energy and Buildings, 42(9), 1628–1636.
9. U.S. Green Building Council. LEED v4 for Building Operations and Maintenance.
10. Clements-Croome, D. (2018). Smart Buildings: Technology and the Design of the Built Environment. Routledge.
11. Ren, G., et al. ML-based occupancy modeling for smart HVAC. Building Simulation, 2021.
12. Persily, A. (1997). Evaluating building IAQ and ventilation with indoor carbon dioxide. ASHRAE Transactions, 103(2).
13. Oldewurtel, F., et al. (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45, 15–27.
14. Killian, M., & Kozek, M. (2016). Implementation of cooperative fuzzy model predictive control for energy-efficient buildings. Applied Energy, 164, 380–395.
15. Wei, T., Wang, Y., & Zhu, Q. (2017). Deep reinforcement learning for building HVAC control. Proceedings of ACM e-Energy 2017.
