Beyond comfort - Managing the impact of HVAC control on the outside world

John K. Ward*, Joshua Wall, Sam West, Richard De Dear

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)

Abstract

CSIRO's Intelligent Energy HVAC research program considers a trichotomy of design principles which must be balanced while providing cost effective operating schedules. Specifically, we consider: 1) Reducing the impact of the HVAC system on the electricity network (demand management); 2) Reducing greenhouse gas emissions; and 3) Achieving thermal comfort for building occupants. This approach has been utilised to develop an intelligent HVAC supervisory control system that utilises multi-agent systems science and machine learning techniques to automatically learn HVAC system behaviour. This is then used to evaluate different control scenarios to determine optimal HVAC control setpoints and operating schedules. Performance is being field verified in commercial building deployments in south-eastern Australia. Theoretical analysis has shown greenhouse gas reductions of up to 30% without loss of thermal comfort, while initial deployments have achieved CO 2 and HVAC energy savings of 10-15% with further savings expected following the full implementation of our system.

Original languageEnglish
Title of host publicationProceedings of Conference: Air Conditioning and the Low Carbon Cooling Challenge - Windsor 2008 Conference
Publication statusPublished - 2008
EventConference on Air Conditioning and the Low Carbon Cooling Challenge - Windsor 2008 Conference - Windsor, United Kingdom
Duration: 27 Jul 200829 Jul 2008

Other

OtherConference on Air Conditioning and the Low Carbon Cooling Challenge - Windsor 2008 Conference
CountryUnited Kingdom
CityWindsor
Period27/07/0829/07/08

Keywords

  • Artificial intelligence
  • Demand management
  • Intelligent HVAC control
  • Machine learning

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