Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm

A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived pre...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 36; no. 10; pp. 5935 - 5943
Main Authors: Kusiak, Andrew, Xu, Guanglin, Tang, Fan
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.10.2011
Elsevier
Subjects:
air conditioning
Algorithms
Applied sciences
Construction
Energy
Energy consumption
Evolutionary computation
Exact sciences and technology
heat
humidity
HVAC
Mathematical models
Neural networks
Neutral network
Optimization
Strength
Strength multi-objective particle-swarm algorithm
temperature
Thermal comfort
Neutral network
HVAC
Strength multi-objective particle-swarm algorithm
Optimization
Evolutionary computation
ISSN:0360-5442
Online Access:Get full text
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Summary:A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption. ►Optimization of a heating, ventilation, and air conditioning system in an office building is presented. ►Relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. ►Control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. ►The solutions derived in the paper represent trade-offs between thermal comfort and energy consumption.
Bibliography:http://dx.doi.org/10.1016/j.energy.2011.08.024
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ISSN:0360-5442
DOI:10.1016/j.energy.2011.08.024