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Modeling and optimization of HVAC systems using a dynamic neural network

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  • Kusiak, Andrew
  • Xu, Guanglin

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.

Suggested Citation

  • Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    • Handle: RePEc:eee:energy:v:42:y:2012:i:1:p:241-250
    DOI: 10.1016/j.energy.2012.03.063
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    References listed on IDEAS

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    1. Gebreslassie, Berhane H. & Guillén-Gosálbez, Gonzalo & Jiménez, Laureano & Boer, Dieter, 2009. "Design of environmentally conscious absorption cooling systems via multi-objective optimization and life cycle assessment," Applied Energy, Elsevier, vol. 86(9), pages 1712-1722, September.
    2. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.
    3. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
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    5. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    6. Ben-Nakhi, Abdullatif E. & Mahmoud, Mohamed A., 2002. "Energy conservation in buildings through efficient A/C control using neural networks," Applied Energy, Elsevier, vol. 73(1), pages 5-23, September.
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