Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements
The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/14/9/2842 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850258327040163840 |
|---|---|
| author | Bin Hu Yuhu Guo Wenjun Huang Jianxiang Jin Mingxuan Zou Zhikun Zhu |
| author_facet | Bin Hu Yuhu Guo Wenjun Huang Jianxiang Jin Mingxuan Zou Zhikun Zhu |
| author_sort | Bin Hu |
| collection | DOAJ |
| description | The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies. |
| format | Article |
| id | doaj-art-c34957e3d6114bf9bbf7fbfef42e051e |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-c34957e3d6114bf9bbf7fbfef42e051e2025-08-20T01:56:12ZengMDPI AGBuildings2075-53092024-09-01149284210.3390/buildings14092842Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with EnhancementsBin Hu0Yuhu Guo1Wenjun Huang2Jianxiang Jin3Mingxuan Zou4Zhikun Zhu5State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, ChinaHuzhou Institute of Industrial Control Technology, Huzhou 313000, ChinaState Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, ChinaZhejiang YunTrol Intelligence Control Technology Co., Ltd., Hangzhou 310053, ChinaThe complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.https://www.mdpi.com/2075-5309/14/9/2842HVAC systemenergy consumption modelALOEswarm intelligence optimization algorithms |
| spellingShingle | Bin Hu Yuhu Guo Wenjun Huang Jianxiang Jin Mingxuan Zou Zhikun Zhu Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements Buildings HVAC system energy consumption model ALOE swarm intelligence optimization algorithms |
| title | Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements |
| title_full | Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements |
| title_fullStr | Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements |
| title_full_unstemmed | Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements |
| title_short | Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements |
| title_sort | energy saving optimization of hvac systems using an ant lion optimizer with enhancements |
| topic | HVAC system energy consumption model ALOE swarm intelligence optimization algorithms |
| url | https://www.mdpi.com/2075-5309/14/9/2842 |
| work_keys_str_mv | AT binhu energysavingoptimizationofhvacsystemsusinganantlionoptimizerwithenhancements AT yuhuguo energysavingoptimizationofhvacsystemsusinganantlionoptimizerwithenhancements AT wenjunhuang energysavingoptimizationofhvacsystemsusinganantlionoptimizerwithenhancements AT jianxiangjin energysavingoptimizationofhvacsystemsusinganantlionoptimizerwithenhancements AT mingxuanzou energysavingoptimizationofhvacsystemsusinganantlionoptimizerwithenhancements AT zhikunzhu energysavingoptimizationofhvacsystemsusinganantlionoptimizerwithenhancements |