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Original Article
Optimized Design and Economic Analysis of an Energy-Efficient HVAC System for a Multi-Story Commercial Building
Dr. Amey S. Wagh1
Shaikh Owais Nazimuddin2
Shaikh Saif Hassan3
Roshan Sitaram Joshi4
Maurya Avnish Raysaheb5
1Assistant Professor, Department of Mechanical Engineering, M.H. Saboo Siddik College of Engineering, Mumbai, India. 2345UG Students, Department of Mechanical Engineering, M.H. Saboo Siddik College of Engineering, Mumbai, India.
Published Online: July-August 2025
Pages: 01-10
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250504001
References
1. American Society of Heating, Refrigerating and Air-Conditioning Engineers. (1996). ASHRAE handbook: HVAC systems and equipment. ASHRAE.
2. Indian Society of Heating, Refrigerating and Air Conditioning Engineers. (2007). ISHRAE handbook. ISHRAE.
3. Honeywell. (2018). Fire detection system technical manual. Honeywell International Inc.
4. Occupational Safety and Health Administration. (2017). OSHA technical manual (OTM). U.S. Department of Labor.
5. Bureau of Indian Standards. (2016). National building code of India. BIS.
6. Air Filtration study to improve indoor air quality: Proposed study, M.D Amir Abdullah, A.M. Leman, A.H. Abdullah, 2014.
7. Yu, L., Sun, Y., Xu, Z., Shen, C., Yue, D., Jiang, T., & Guan, X. (2020). Multi agent deep reinforcement learning for HVAC control in commercial buildings. IEEE Transactions on Smart Grid. Advance online publication. https://doi.org/10.1109/TSG.2020.3011739
8. Zhang, S., Ai, Z., & Lin, Z. (2021). Novel demand controlled optimization of constant air volume mechanical ventilation for indoor air quality, durability and energy saving. Applied Energy, 293, 116954. https://doi.org/10.1016/j.apenergy.2021.116954
9. Wang, H., Chen, X., Vital, N., Duffy, E., & Razi, A. (2024). Energy optimization for HVAC systems in multi VAV open office spaces using deep reinforcement learning. Applied Energy, 356, 122354. https://doi.org/10.1016/j.apenergy.2023.122354
10. Cui, C., & Zhang, X. (2020). An energy saving oriented air balancing method for demand controlled ventilation systems with branch and black box model. Applied Energy, 264, 114734. https://doi.org/10.1016/j.apenergy.2020.114734
11. Kim, J., Cara Donna, C., & Parker, A. (2024). End-use savings shapes measure documentation: variable refrigerant flow with heat recovery and dedicated outdoor air system. NREL Technical Report. https://doi.org/10.2172/2356776
12. Hanumaiah, V., & Genc, S. (2021). Distributed multi agent deep reinforcement learning framework for whole building HVAC control. arXiv preprint arXiv:2110.13450. https://doi.org/10.48550/arXiv.2110.13450
13. Imal, M. (2015). Design and implementation of energy efficiency in HVAC systems based on robust PID control for industrial applications. Journal of Sensors, 2015, Article 954159. https://doi.org/10.1155/2015/954159
2. Indian Society of Heating, Refrigerating and Air Conditioning Engineers. (2007). ISHRAE handbook. ISHRAE.
3. Honeywell. (2018). Fire detection system technical manual. Honeywell International Inc.
4. Occupational Safety and Health Administration. (2017). OSHA technical manual (OTM). U.S. Department of Labor.
5. Bureau of Indian Standards. (2016). National building code of India. BIS.
6. Air Filtration study to improve indoor air quality: Proposed study, M.D Amir Abdullah, A.M. Leman, A.H. Abdullah, 2014.
7. Yu, L., Sun, Y., Xu, Z., Shen, C., Yue, D., Jiang, T., & Guan, X. (2020). Multi agent deep reinforcement learning for HVAC control in commercial buildings. IEEE Transactions on Smart Grid. Advance online publication. https://doi.org/10.1109/TSG.2020.3011739
8. Zhang, S., Ai, Z., & Lin, Z. (2021). Novel demand controlled optimization of constant air volume mechanical ventilation for indoor air quality, durability and energy saving. Applied Energy, 293, 116954. https://doi.org/10.1016/j.apenergy.2021.116954
9. Wang, H., Chen, X., Vital, N., Duffy, E., & Razi, A. (2024). Energy optimization for HVAC systems in multi VAV open office spaces using deep reinforcement learning. Applied Energy, 356, 122354. https://doi.org/10.1016/j.apenergy.2023.122354
10. Cui, C., & Zhang, X. (2020). An energy saving oriented air balancing method for demand controlled ventilation systems with branch and black box model. Applied Energy, 264, 114734. https://doi.org/10.1016/j.apenergy.2020.114734
11. Kim, J., Cara Donna, C., & Parker, A. (2024). End-use savings shapes measure documentation: variable refrigerant flow with heat recovery and dedicated outdoor air system. NREL Technical Report. https://doi.org/10.2172/2356776
12. Hanumaiah, V., & Genc, S. (2021). Distributed multi agent deep reinforcement learning framework for whole building HVAC control. arXiv preprint arXiv:2110.13450. https://doi.org/10.48550/arXiv.2110.13450
13. Imal, M. (2015). Design and implementation of energy efficiency in HVAC systems based on robust PID control for industrial applications. Journal of Sensors, 2015, Article 954159. https://doi.org/10.1155/2015/954159
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