Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlus
Energy is a critical resource, and its optimization is central to sustainable building design. Occupant comfort, significantly influenced by factors, including mean radiant temperature (MRT), alongside air temperature, velocity, and humidity, is another key consideration. This paper introduces a hyb...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/14/2568 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849246408384708608 |
|---|---|
| author | Reza Akraminejad Tianyi Zhao Yacine Rezgui Ali Ghoroghi Yousef Shahbazi Razlighi |
| author_facet | Reza Akraminejad Tianyi Zhao Yacine Rezgui Ali Ghoroghi Yousef Shahbazi Razlighi |
| author_sort | Reza Akraminejad |
| collection | DOAJ |
| description | Energy is a critical resource, and its optimization is central to sustainable building design. Occupant comfort, significantly influenced by factors, including mean radiant temperature (MRT), alongside air temperature, velocity, and humidity, is another key consideration. This paper introduces a hybrid crow search optimization (CSA) and penguin search optimization algorithm (PeSOA), termed (HCRPN), designed to simultaneously optimize building energy consumption and achieve MRT levels conducive to thermal comfort by adjusting HVAC system parameters. We first validate HCRPN using ZDT-1 and Shaffer N1 multi-objective benchmarks. Subsequently, we employ EnergyPlus simulations, utilizing a single-objective Particle Swarm Optimization (PSO) for initial parameter analysis to generate a dataset. Following correlation analyses to understand parameter relationships, we implement our hybrid multi-objective approach. Comparative evaluations against state-of-the-art algorithms, including MoPso, NSGA-II, hybrid Nsga2/MOEAD, and Mo-CSA, validated the effectiveness of HCRPN. Our findings demonstrate an average 7% reduction in energy consumption and a 3% improvement in MRT-based comfort relative to existing methods. While seemingly small, even minor enhancements in MRT can have a noticeable positive impact on well-being, particularly in large, high-occupancy buildings. |
| format | Article |
| id | doaj-art-8f515afbe89940828be690e7904e3d1e |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-8f515afbe89940828be690e7904e3d1e2025-08-20T03:58:30ZengMDPI AGBuildings2075-53092025-07-011514256810.3390/buildings15142568Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlusReza Akraminejad0Tianyi Zhao1Yacine Rezgui2Ali Ghoroghi3Yousef Shahbazi Razlighi4Department of Computer Engineering, University of Science and Culture, Tehran 1461968151, IranInstitute of Building Energy, Dalian University of Technology, Dalian 116024, ChinaSchool of Engineering, Cardiff University, Cardiff CF24 3AA, UKSchool of Engineering, Cardiff University, Cardiff CF24 3AA, UKDepartment of Civil Engineering, University of Science and Culture, Tehran 1461968151, IranEnergy is a critical resource, and its optimization is central to sustainable building design. Occupant comfort, significantly influenced by factors, including mean radiant temperature (MRT), alongside air temperature, velocity, and humidity, is another key consideration. This paper introduces a hybrid crow search optimization (CSA) and penguin search optimization algorithm (PeSOA), termed (HCRPN), designed to simultaneously optimize building energy consumption and achieve MRT levels conducive to thermal comfort by adjusting HVAC system parameters. We first validate HCRPN using ZDT-1 and Shaffer N1 multi-objective benchmarks. Subsequently, we employ EnergyPlus simulations, utilizing a single-objective Particle Swarm Optimization (PSO) for initial parameter analysis to generate a dataset. Following correlation analyses to understand parameter relationships, we implement our hybrid multi-objective approach. Comparative evaluations against state-of-the-art algorithms, including MoPso, NSGA-II, hybrid Nsga2/MOEAD, and Mo-CSA, validated the effectiveness of HCRPN. Our findings demonstrate an average 7% reduction in energy consumption and a 3% improvement in MRT-based comfort relative to existing methods. While seemingly small, even minor enhancements in MRT can have a noticeable positive impact on well-being, particularly in large, high-occupancy buildings.https://www.mdpi.com/2075-5309/15/14/2568building energy optimizationEnergyPlusthermal comfortmean radiant temperaturehybrid meta-heuristiccrow search algorithm |
| spellingShingle | Reza Akraminejad Tianyi Zhao Yacine Rezgui Ali Ghoroghi Yousef Shahbazi Razlighi Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlus Buildings building energy optimization EnergyPlus thermal comfort mean radiant temperature hybrid meta-heuristic crow search algorithm |
| title | Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlus |
| title_full | Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlus |
| title_fullStr | Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlus |
| title_full_unstemmed | Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlus |
| title_short | Hybrid Metaheuristic Optimization of HVAC Energy Consumption and Thermal Comfort in an Office Building Using EnergyPlus |
| title_sort | hybrid metaheuristic optimization of hvac energy consumption and thermal comfort in an office building using energyplus |
| topic | building energy optimization EnergyPlus thermal comfort mean radiant temperature hybrid meta-heuristic crow search algorithm |
| url | https://www.mdpi.com/2075-5309/15/14/2568 |
| work_keys_str_mv | AT rezaakraminejad hybridmetaheuristicoptimizationofhvacenergyconsumptionandthermalcomfortinanofficebuildingusingenergyplus AT tianyizhao hybridmetaheuristicoptimizationofhvacenergyconsumptionandthermalcomfortinanofficebuildingusingenergyplus AT yacinerezgui hybridmetaheuristicoptimizationofhvacenergyconsumptionandthermalcomfortinanofficebuildingusingenergyplus AT alighoroghi hybridmetaheuristicoptimizationofhvacenergyconsumptionandthermalcomfortinanofficebuildingusingenergyplus AT yousefshahbazirazlighi hybridmetaheuristicoptimizationofhvacenergyconsumptionandthermalcomfortinanofficebuildingusingenergyplus |