Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization
In energy management system (EMS), the scheduling of air-conditioning (AC) system has been shown to reduce considerable amount of its power consumption with relatively low implementation cost. However, most scheduling methods lack a systematic approach to ensuring optimal power consumption reduction...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2018
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/5556/ http://eprints.uthm.edu.my/5556/1/AJ%202018%20%28877%29%20Optimized%20scheduling%20for%20an%20airconditioning%20system%20based%20on%20indoor%20thermal%20comfort%20using%20the%20multiobjective%20improved%20global%20particle%20swa.pdf |
| _version_ | 1848888582115164160 |
|---|---|
| author | Haniff, Mohamad Fadzli Selamat, Hazlina Khamis, Nuraqilla Alimin, Ahmad Jais |
| author_facet | Haniff, Mohamad Fadzli Selamat, Hazlina Khamis, Nuraqilla Alimin, Ahmad Jais |
| author_sort | Haniff, Mohamad Fadzli |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | In energy management system (EMS), the scheduling of air-conditioning (AC) system has been shown to reduce considerable amount of its power consumption with relatively low implementation cost. However, most scheduling methods lack a systematic approach to ensuring optimal power consumption reduction and comfort experienced by occupants. The main contribution of this paper is a new optimized AC scheduling approach that focuses on indoor thermal comfort using a new multi-objective optimization algorithm, called the improved global particle swarm optimization (IGPSO), which able to find better optimal solutions faster than its original version, the global particle swarm optimization (GPSO) algorithm. IGPSO is used to model the building characteristics and to find optimum indoor temperature values for the room/building. The proposed technique is based on predicted mean vote (PMV) comfort index that is able to reduce AC power consumption while maintaining indoor comfort throughout its operation. The schedule is set in advance by making use of weather forecast and the estimation of building characteristic parameters. This technique can be implemented on existing buildings with existing HVAC systems with minimal modifications to the HVAC infrastructure. Experimental results show that the proposed method is able to provide good PMV while consuming less power compared to the commonly used extended pre-cooling technique. |
| first_indexed | 2025-11-15T20:12:34Z |
| format | Article |
| id | uthm-5556 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:12:34Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-55562022-01-17T01:09:43Z http://eprints.uthm.edu.my/5556/ Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization Haniff, Mohamad Fadzli Selamat, Hazlina Khamis, Nuraqilla Alimin, Ahmad Jais TH7005-7699 Heating and ventilation. Air conditioning In energy management system (EMS), the scheduling of air-conditioning (AC) system has been shown to reduce considerable amount of its power consumption with relatively low implementation cost. However, most scheduling methods lack a systematic approach to ensuring optimal power consumption reduction and comfort experienced by occupants. The main contribution of this paper is a new optimized AC scheduling approach that focuses on indoor thermal comfort using a new multi-objective optimization algorithm, called the improved global particle swarm optimization (IGPSO), which able to find better optimal solutions faster than its original version, the global particle swarm optimization (GPSO) algorithm. IGPSO is used to model the building characteristics and to find optimum indoor temperature values for the room/building. The proposed technique is based on predicted mean vote (PMV) comfort index that is able to reduce AC power consumption while maintaining indoor comfort throughout its operation. The schedule is set in advance by making use of weather forecast and the estimation of building characteristic parameters. This technique can be implemented on existing buildings with existing HVAC systems with minimal modifications to the HVAC infrastructure. Experimental results show that the proposed method is able to provide good PMV while consuming less power compared to the commonly used extended pre-cooling technique. Springer 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5556/1/AJ%202018%20%28877%29%20Optimized%20scheduling%20for%20an%20airconditioning%20system%20based%20on%20indoor%20thermal%20comfort%20using%20the%20multiobjective%20improved%20global%20particle%20swa.pdf Haniff, Mohamad Fadzli and Selamat, Hazlina and Khamis, Nuraqilla and Alimin, Ahmad Jais (2018) Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization. Energy Efficiency, 12. pp. 1183-1201. ISSN 1570-6478 |
| spellingShingle | TH7005-7699 Heating and ventilation. Air conditioning Haniff, Mohamad Fadzli Selamat, Hazlina Khamis, Nuraqilla Alimin, Ahmad Jais Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization |
| title | Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization |
| title_full | Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization |
| title_fullStr | Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization |
| title_full_unstemmed | Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization |
| title_short | Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization |
| title_sort | optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization |
| topic | TH7005-7699 Heating and ventilation. Air conditioning |
| url | http://eprints.uthm.edu.my/5556/ http://eprints.uthm.edu.my/5556/1/AJ%202018%20%28877%29%20Optimized%20scheduling%20for%20an%20airconditioning%20system%20based%20on%20indoor%20thermal%20comfort%20using%20the%20multiobjective%20improved%20global%20particle%20swa.pdf |