Journal article
Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm
Energy (Oxford), Vol.36(10), pp.5935-5943
2011
DOI: 10.1016/j.energy.2011.08.024
Abstract
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.
Details
- Title: Subtitle
- Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm
- Creators
- Andrew KUSIAK - Department of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, United StatesGuanglin Xu - Department of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, United StatesFan Tang - Department of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, United States
- Resource Type
- Journal article
- Publication Details
- Energy (Oxford), Vol.36(10), pp.5935-5943
- Publisher
- Elsevier; Kidlington
- DOI
- 10.1016/j.energy.2011.08.024
- ISSN
- 0360-5442
- eISSN
- 1873-6785
- Language
- English
- Date published
- 2011
- Academic Unit
- Biostatistics; Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984064202702771
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