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...

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Vydané v:Buildings (Basel) Ročník 14; číslo 9; s. 2842
Hlavní autori: Hu, Bin, Guo, Yuhu, Huang, Wenjun, Jin, Jianxiang, Zou, Mingxuan, Zhu, Zhikun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2024
Predmet:
Accuracy
Air conditioning
Algorithms
ALOE
Carbon
Case studies
China
Climatic conditions
Cooling
Efficiency
Emissions
Energy conservation
Energy consumption
energy consumption model
Energy industry
Energy management systems
Energy trading
Energy use
Green buildings
HVAC
HVAC equipment
HVAC system
Linear programming
Load distribution
Machine learning
Mathematical models
Mathematical optimization
Methods
Office buildings
Optimization
Optimization algorithms
Parameter identification
Population studies
Swarm intelligence
swarm intelligence optimization algorithms
Temperature
China
ISSN:2075-5309, 2075-5309
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings14092842