Journal article
Modeling and optimization of HVAC systems using a dynamic neural network
Energy (Oxford), Vol.42(1), pp.241-250
2012
DOI: 10.1016/j.energy.2012.03.063
Abstract
The energy consumption of a heating, ventilating and air conditioning (HVAC) system is optimized by using a data-driven approach. Predictive models with controllable and uncontrollable input and output variables utilize the concept of a dynamic neural network. The minimization of the energy consumed while maintaining indoor room temperature at an acceptable level is accomplished with a bi-objective optimization. The model is solved with three variants of the multi-objective particle swarm optimization algorithm. The optimization model and the multi-objective algorithm have been implemented in an existing HVAC system. The test results performed in the existing environment demonstrate significant improvement of the system. Compared to the traditional control strategy, the proposed model saved up to 30% of energy.
Details
- Title: Subtitle
- Modeling and optimization of HVAC systems using a dynamic neural network
- 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 States
- Resource Type
- Journal article
- Publication Details
- Energy (Oxford), Vol.42(1), pp.241-250
- Publisher
- Elsevier; Kidlington
- DOI
- 10.1016/j.energy.2012.03.063
- ISSN
- 0360-5442
- eISSN
- 1873-6785
- Language
- English
- Date published
- 2012
- Academic Unit
- Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984064217402771
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