HVAC SYSTEM MODELING AND INTELLIGENT CONTROL TECHNIQUES FOR ENERGY OPTIMIZATION IN BUILDINGS
Main Article Content
Abstract
The rapid growth in energy consumption by buildings and the limitations of conventional HVAC control systems have created an urgent need for more efficient and intelligent solutions. Conventional control methods do not keep up with dynamic occupancy and environmental variability, and complicated thermal interactions, and result in unreasonable energy consumption and decreased comfort. In the paper, the detailed overview of the approaches of HVAC system modeling and intelligent methodology of building energy optimization is described. The paper is a systematic review of the basis of HVAC, system components, centralized, decentralized, and hybrid systems. Various gray-box models are introduced and examined with regard to their utility in predicting thermal behavior and making decisions pertaining to controls. These models include physics-based, data-driven, and hybrid versions. Fuzzy logic, ML, DL, and RL are some of the smart control technologies that are compared to more traditional PID-based methods. Energy efficiency and thermal comfort can be achieved with smart HVAC control, according to recent literature. The paper also points out major challenges, limitations and possible research directions to building adaptive, data-driven and sustainable HVAC system in the modern buildings.
Article Details
Section
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
[1] B. Hakawati, A. Mousa, and F. Draidi, “Smart energy management in residential buildings : the impact of knowledge and behavior,” Sci. Rep., pp. 1–15, 2024, doi: 10.1038/s41598-024-51638-y.
[2] A. F. Santos et al., “The Importance of the HVAC-R Area (Heating, Ventilation, Air Conditioning and Refrigeration) in the World and National Sphere and Actions to Reduce Energy Consumption as Heat Recovery,” J. Eng. Res., vol. 2, no. 17, pp. 2–10, Aug. 2022, doi: 10.22533/at.ed.31721722020811.
[3] R. Patel and R. Tandon, “Advancements in Data Center Engineering: Optimizing Thermal Management, HVAC Systems, and Structural Reliability,” Int. J. Res. Anal. Rev., vol. 8, no. 2, pp. 991–996, 2021.
[4] D. R. Mckoy, R. C. Tesiero, Y. T. Acquaah, and B. Gokaraju, “Review of HVAC Systems History and Future Applications,” Energies, vol. 16, no. 17, 2023, doi: 10.3390/en16176109.
[5] V. Mistry, “Energy Efficiency in HVAC Systems through Building Automation,” J. Biosens. Bioelectron. Res., pp. 1–4, Mar. 2023, doi: 10.47363/JBBER/2023(1)113.
[6] X. Zhou, Y. Liu, M. Luo, L. Zhang, Q. Zhang, and X. Zhang, “Thermal comfort under radiant asymmetries of floor cooling system in 2 h and 8 h exposure durations,” Energy Build., vol. 188, pp. 98–110, 2019.
[7] K. M. R. Seetharaman and S. Pandya, “Importance Of Artificial Intelligence In Transforming Sales, Procurement, And Supply Chain Processes,” Int. J. Recent Technol. Sci. Manag., vol. 8, no. 7, pp. 1–9, 2023.
[8] N. Malali and S. R. Praveen Madugula, “Robustness and Adversarial Resilience of Actuarial AI/ML Models in the Face of Evolving Threats,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 3, pp. 910–916, Mar. 2025, doi: 10.38124/ijisrt/25mar1287.
[9] S. Seyam, “Types of HVAC Systems,” in HVAC System, InTech, 2018. doi: 10.5772/intechopen.78942.
[10] V. Verma, “Big Data and Cloud Databases Revolutionizing Business Intelligence,” TIJER – Int. Res. J., vol. 9, no. 1, 2022.
[11] W. W. Che et al., “Energy consumption, indoor thermal comfort and air quality in a commercial office with retrofitted heat, ventilation and air conditioning (HVAC) system,” Energy Build., vol. 201, pp. 202–215, 2019.
[12] M. Hamza, O. Bafail, and H. Alidrisi, “HVAC Systems Evaluation and Selection for Sustainable Office Buildings: An Integrated MCDM Approach,” Buildings, vol. 13, no. 7, 2023, doi: 10.3390/buildings13071847.
[13] M. Esrafilian-Najafabadi and F. Haghighat, “Occupancy-based HVAC control systems in buildings: A state-of-the-art review,” Build. Environ., vol. 197, p. 107810, 2021.
[14] P. Ebrahimi, I. Ridwana, and N. Nassif, “Solutions to Achieve High-Efficient and Clean Building HVAC Systems,” Buildings, vol. 13, no. 5, 2023, doi: 10.3390/buildings13051211.
[15] R. Srinivasan et al., “Preventive Maintenance of Centralized HVAC Systems: Use of Acoustic Sensors, Feature Extraction, and Unsupervised Learning,” Aug. 2017. doi: 10.26868/25222708.2017.715.
[16] S. Gupta et al., “Water-Energy Correlations: Analysis of Water Technologies, Processes and Systems in Rural and Urban India,” in 2015 AGU Fall Meeting, 2015.
[17] P. Patel, “Predictive Maintenance in HVAC Systems Using Machine Learning Algorithms: A Comparative Study,” Int. J. Eng. Sci. Math., vol. 9, no. 11, pp. 118–125, 2024.
[18] S. Dawood, A. Hatami, and R. Homod, “HVAC system modeling and control methods: a review and case study,” 2025. doi: 10.22109/jemt.2022.298902.1324.
[19] Z. Afroz, G. Shafiullah, T. Urmee, and G. Higgins, “Modeling techniques used in building HVAC control systems: A review,” Renew. Sustain. Energy Rev., vol. 83, no. June, pp. 64–84, Mar. 2018, doi: 10.1016/j.rser.2017.10.044.
[20] V. Panchal, “Thermal and Power Management Challenges in High-Performance Mobile Processors,” Int. J. Innov. Res. Sci. Eng. Technol., vol. 13, no. 11, Nov. 2024, doi: 10.15680/IJIRSET.2024.1311014.
[21] G. Mosaico, M. Saviozzi, F. Silvestro, A. Bagnasco, and A. Vinci, “Simplified State Space Building Energy Model and Transfer Learning Based Occupancy Estimation for HVAC Optimal Control,” 2019 IEEE 5th Int. forum Res. Technol. Soc. Ind., pp. 353–358, 2019, doi: 10.1109/RTSI.2019.8895544.
[22] N. Prajapati, “The Role of Machine Learning in Big Data Analytics: Tools, Techniques, and Applications,” ESP J. Eng. Technol. Adv., vol. 5, no. 2, pp. 16–22, 2025, doi: 10.56472/25832646/JETA-V5I2P103.
[23] Y. Macha and S. K. Pulichikkunnu, “A Survey of DevOps Practices for Machine Learning and Artificial Intelligence Workflows in Modern Software Development,” ESP J. Eng. Technol. Adv., vol. 4, no. 3, pp. 200–208, 2024, doi: 10.56472/25832646/JETA-V4I3P121.
[24] N. M.-A. Mutombo and B. P. Numbi, “Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load,” Energies, vol. 15, no. 14, 2022, doi: 10.3390/en15145097.
[25] V. Prajapati, “Enhancing Threat Intelligence and Cyber Defense through Big Data Analytics: A Review Study,” J. Glob. Res. Math. Arch., vol. 12, no. 4, pp. 1–10, 2025.
[26] F. Behrooz, N. Mariun, M. H. Marhaban, M. A. M. Radzi, and A. R. Ramli, “Review of control techniques for HVAC systems-nonlinearity approaches based on fuzzy cognitive maps,” Energies, 2018, doi: 10.3390/en11030495.
[27] A. Yayla et al., “Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings,” Sustainability, vol. 14, no. 23, 2022, doi: 10.3390/su142316107.
[28] T. Olatunde, A. Okwandu, D. Akande, and Z. Sikhakhane, “Review of energy-efficient HVAC technologies for sustainable buildings,” Int. J. Sci. Technol. Res. Arch., vol. 6, pp. 12–20, 2024, doi: 10.53771/ijstra.2024.6.2.0039.
[29] R. Patel, “A Survey on PLC Programming Languages and Their Applications in Modern Industrial Automation Industry,” Int. J. Eng. Sci. Math., vol. 10, no. 8, pp. 129–136, 2021.
[30] M. R. R. Deva and N. Jain, “Utilizing Azure Automated Machine Learning and XGBoost for Predicting Cloud Resource Utilization in Enterprise Environments,” in 2025 International Conference on Networks and Cryptology (NETCRYPT), IEEE, May 2025, pp. 535–540. doi: 10.1109/NETCRYPT65877.2025.11102235.
[31] A. Gharbi, M. Ayari, N. Albalawi, Y. El Touati, and Z. Klai, “Intelligent HVAC Control: Comparative Simulation of Reinforcement Learning and PID Strategies for Energy Efficiency and Comfort Optimization,” Mathematics, vol. 13, no. 14, 2025, doi: 10.3390/math13142311.
[32] R. Jin, P. Xu, J. Gu, T. Xiao, C. Li, and H. Wang, “Review of optimization control methods for HVAC systems in Demand Response (DR): Transition from model-driven to model-free approaches and challenges,” Build. Environ., vol. 280, p. 113045, 2025, doi: https://doi.org/10.1016/j.buildenv.2025.113045.
[33] J. Li, M. Wang, P. Fang, Q. Zhao, X. Zheng, and H. Gao, “Modeling and optimization of a passive building HVAC system based on improved PSO and H-J Algorithm,” Energy Reports, vol. 11, pp. 2415–2438, Jun. 2024, doi: 10.1016/j.egyr.2024.02.011.
[34] R. Loggia, A. Flamini, A. Galasso, R. Menichelli, C. Moscatiello, and L. Martirano, “Impact of Building Automation, Controls and Building Management on the Energy Performance of Buildings,” in 2024 IEEE Industry Applications Society Annual Meeting (IAS), 2024, pp. 1–8. doi: 10.1109/IAS55788.2024.11023817.
[35] H. M. N. B. Herath, P. Paranamana, and D. C. N. Chandrasena, “Development of A Demand Control Algorithm for HVAC Systems to Conserve Energy in Office Buildings,” in 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023, pp. 1–6. doi: 10.1109/ETFG55873.2023.10407435.
[36] Y. E. Isikdemir, G. Erturk, H. Ates, and M. O. Tas, “Fuzzy Inference and Machine Learning Based HVAC Control System for Smart Buildings,” in 2022 Global Energy Conference (GEC), 2022, pp. 116–119. doi: 10.1109/GEC55014.2022.9987083.