Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings
This research utilizes swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and hybrid PSO-ACO-to optimize energy efficiency and thermal comfort in smart building HVAC systems. A thorough experimental analysis was done in a 500-kW cooling capacity smart bui...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25010834 |
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| author | Vinoth Kanna I Raja Subramani Maher Ali Rusho Shubham Sharma Ramachandran T Abinash Mahapatro Deepak Gupta Jasmina Lozanovic |
| author_facet | Vinoth Kanna I Raja Subramani Maher Ali Rusho Shubham Sharma Ramachandran T Abinash Mahapatro Deepak Gupta Jasmina Lozanovic |
| author_sort | Vinoth Kanna I |
| collection | DOAJ |
| description | This research utilizes swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and hybrid PSO-ACO-to optimize energy efficiency and thermal comfort in smart building HVAC systems. A thorough experimental analysis was done in a 500-kW cooling capacity smart building in 20 monitored temperature zones for 12 months. The hybrid PSO-ACO model performed the best energy savings of 28.9 %, better than PSO (23.7 %) and ACO (20.5 %), and also saving 29.2 % peak load demand. Thermal comfort analysis through Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) metrics showed better indoor conditions, with the hybrid model keeping room temperatures within ±0.8 °C of the setpoint and bringing the PPD index down to 8.5 %. Statistical validation through ANOVA and t-tests supported the energy and comfort gains with p-values always less than 0.05. The hybrid model also exhibited faster convergence, completing the optimization in 120 iterations, 20 % faster than ACO. Economic analysis estimated annual cost savings of $12,000 for a 10,000 m2 building with a return on investment within 2.5 years, while saving 35.2 metric tons of CO2 emissions per year. The results illustrate the outstanding performance of hybrid swarm intelligence algorithms to improve HVAC efficiency and occupant comfort, a scalable and inexpensive solution for smart building management. Future research will utilize machine learning combined with swarm intelligence for adaptive and predictive HVAC control. |
| format | Article |
| id | doaj-art-aa57ee220c5445a28e806b3f77d6e893 |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-aa57ee220c5445a28e806b3f77d6e8932025-08-24T05:12:42ZengElsevierCase Studies in Thermal Engineering2214-157X2025-10-017410682310.1016/j.csite.2025.106823Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildingsVinoth Kanna I0Raja Subramani1Maher Ali Rusho2Shubham Sharma3Ramachandran T4Abinash Mahapatro5Deepak Gupta6Jasmina Lozanovic7Center for Advanced Multidisciplinary Research and Innovation, Chennai Institute of Technology, Chennai, Tamilnadu, India, 600069Center for Advanced Multidisciplinary Research and Innovation, Chennai Institute of Technology, Chennai, Tamilnadu, India, 600069; Corresponding author.Lockheed Martin Engineering Management, University of Colorado, Boulder, CO, 80308, USADepartment of Mechanical Engineering, Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, 201306, India; Department of Technical Sciences, Western Caspian University, Baku, Azerbaijan; Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India; Corresponding author. Department of Mechanical Engineering, Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, 201306, India.Department of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaDepartment of Mechanical Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, IndiaDepartment of Mechanical Engineering, Graphic Era Hill University, Dehradun, 248002, Uttarakhand, India; Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, 248002, IndiaHochschule Campus Wien (HCW) - University of Applied Sciences, Department of Engineering, Clinical Engineering, Favoritenstraße 226, 1100, Vienna, Austria; Corresponding author.This research utilizes swarm intelligence algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and hybrid PSO-ACO-to optimize energy efficiency and thermal comfort in smart building HVAC systems. A thorough experimental analysis was done in a 500-kW cooling capacity smart building in 20 monitored temperature zones for 12 months. The hybrid PSO-ACO model performed the best energy savings of 28.9 %, better than PSO (23.7 %) and ACO (20.5 %), and also saving 29.2 % peak load demand. Thermal comfort analysis through Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) metrics showed better indoor conditions, with the hybrid model keeping room temperatures within ±0.8 °C of the setpoint and bringing the PPD index down to 8.5 %. Statistical validation through ANOVA and t-tests supported the energy and comfort gains with p-values always less than 0.05. The hybrid model also exhibited faster convergence, completing the optimization in 120 iterations, 20 % faster than ACO. Economic analysis estimated annual cost savings of $12,000 for a 10,000 m2 building with a return on investment within 2.5 years, while saving 35.2 metric tons of CO2 emissions per year. The results illustrate the outstanding performance of hybrid swarm intelligence algorithms to improve HVAC efficiency and occupant comfort, a scalable and inexpensive solution for smart building management. Future research will utilize machine learning combined with swarm intelligence for adaptive and predictive HVAC control.http://www.sciencedirect.com/science/article/pii/S2214157X25010834Swarm intelligenceParticle swarm optimizationAnt colony optimizationHVAC optimizationEnergy efficiencySmart buildings |
| spellingShingle | Vinoth Kanna I Raja Subramani Maher Ali Rusho Shubham Sharma Ramachandran T Abinash Mahapatro Deepak Gupta Jasmina Lozanovic Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings Case Studies in Thermal Engineering Swarm intelligence Particle swarm optimization Ant colony optimization HVAC optimization Energy efficiency Smart buildings |
| title | Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings |
| title_full | Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings |
| title_fullStr | Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings |
| title_full_unstemmed | Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings |
| title_short | Swarm intelligence for energy-efficient heating, ventilation, and air conditioning (HVAC) systems: A case study in smart buildings |
| title_sort | swarm intelligence for energy efficient heating ventilation and air conditioning hvac systems a case study in smart buildings |
| topic | Swarm intelligence Particle swarm optimization Ant colony optimization HVAC optimization Energy efficiency Smart buildings |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25010834 |
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