File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Using genetic algorithms to optimize controller parameters for HVAC systems

TitleUsing genetic algorithms to optimize controller parameters for HVAC systems
Authors
KeywordsControl
Genetic Algorithm
Hvac Systems
Optimization
Simulation
Issue Date1997
PublisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/enbuild
Citation
Energy And Buildings, 1997, v. 26 n. 3, p. 277-282 How to Cite?
AbstractThis paper presents an adaptive learning algorithm based on genetic algorithms (GA) for automatic tuning of proportional, integral and derivative (PID) controllers in Heating Ventilating and Air Conditioning (HVAC) systems to achieve optimal performance. Genetic algorithms, which are search procedures based on the mechanics of Darwin's natural selection, are employed since they have been proved to be robust and efficient in finding near-optimal solutions in complex problem spaces. The modular dynamic simulation software package HVACSIM + has been modified and incorporated in the genetic algorithm-based optimization program to provide a complete simulation environment for detailed study of controller performance. Three performance indicators - overshoot, settling time, and mean squared error -are considered in the objective function of the optimization procedure for evaluation of controller performance. The simulation results show that the genetic algorithm-based optimization procedures as implemented in this research study are useful for automatic tuning of PID controllers for HVAC systems, yielding minimum overshoot and minimum settling time. © 1997 Elsevier Science S.A.
Persistent Identifierhttp://hdl.handle.net/10722/156471
ISSN
2015 Impact Factor: 2.973
2015 SCImago Journal Rankings: 2.073
References

 

DC FieldValueLanguage
dc.contributor.authorHuang, Wen_US
dc.contributor.authorLam, HNen_US
dc.date.accessioned2012-08-08T08:42:33Z-
dc.date.available2012-08-08T08:42:33Z-
dc.date.issued1997en_US
dc.identifier.citationEnergy And Buildings, 1997, v. 26 n. 3, p. 277-282en_US
dc.identifier.issn0378-7788en_US
dc.identifier.urihttp://hdl.handle.net/10722/156471-
dc.description.abstractThis paper presents an adaptive learning algorithm based on genetic algorithms (GA) for automatic tuning of proportional, integral and derivative (PID) controllers in Heating Ventilating and Air Conditioning (HVAC) systems to achieve optimal performance. Genetic algorithms, which are search procedures based on the mechanics of Darwin's natural selection, are employed since they have been proved to be robust and efficient in finding near-optimal solutions in complex problem spaces. The modular dynamic simulation software package HVACSIM + has been modified and incorporated in the genetic algorithm-based optimization program to provide a complete simulation environment for detailed study of controller performance. Three performance indicators - overshoot, settling time, and mean squared error -are considered in the objective function of the optimization procedure for evaluation of controller performance. The simulation results show that the genetic algorithm-based optimization procedures as implemented in this research study are useful for automatic tuning of PID controllers for HVAC systems, yielding minimum overshoot and minimum settling time. © 1997 Elsevier Science S.A.en_US
dc.languageengen_US
dc.publisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/enbuilden_US
dc.relation.ispartofEnergy and Buildingsen_US
dc.subjectControlen_US
dc.subjectGenetic Algorithmen_US
dc.subjectHvac Systemsen_US
dc.subjectOptimizationen_US
dc.subjectSimulationen_US
dc.titleUsing genetic algorithms to optimize controller parameters for HVAC systemsen_US
dc.typeArticleen_US
dc.identifier.emailLam, HN:hremlhn@hkucc.hku.hken_US
dc.identifier.authorityLam, HN=rp00132en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0031245548en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0031245548&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume26en_US
dc.identifier.issue3en_US
dc.identifier.spage277en_US
dc.identifier.epage282en_US
dc.publisher.placeSwitzerlanden_US
dc.identifier.scopusauthoridHuang, W=7407904334en_US
dc.identifier.scopusauthoridLam, HN=7202774923en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats