Modeling and optimization of HVAC systems using a dynamic neural network

Andrew Kusiak, Guanglin Xu

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)241-250
Number of pages10
Issue number1
StatePublished - Jun 2012

Bibliographical note

Funding Information:
This research has been supported by the Iowa Energy Center , Grant No. 08-01 .

Copyright 2017 Elsevier B.V., All rights reserved.


  • Data-driven modeling
  • Dynamic neutral network
  • HVAC
  • Multi-objective particle swarm optimization
  • Non-linear model


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