Multi-objective optimization of HVAC system with an evolutionary computation algorithm

Andrew Kusiak, Fan Tang, Guanglin Xu

Research output: Contribution to journalArticlepeer-review

124 Scopus citations

Abstract

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables - supply air temperature and supply air duct static pressure set points - are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.

Original languageEnglish (US)
Pages (from-to)2440-2449
Number of pages10
JournalEnergy
Volume36
Issue number5
DOIs
StatePublished - May 2011

Keywords

  • Data-driven models
  • Energy savings
  • Evolutionary computation algorithm
  • HVAC system optimization

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