AccuRate predictions compared with monitored data
24 June, 2008
In order to validate the heating and cooling energy requirements predicted by AccuRate, a comparison was made with detailed monitoring of heating and cooling energy data for 6 suburban Adelaide houses over 2 years.
This study was conducted at the Sustainable Energy Centre, University of South Australia, by Professor Wasim Saman, Associate Professor Monica Oliphant, Mr. Lachlan Mudge and Dr. Edward Halawa.
After allowing for the appliance energy performance, the study demonstrated the ability to utilise AccuRate Australian Edition in determining the monthly heating requirements. However, AccuRate, initially under predicted the cooling energy consumption.
In an attempt to identify the quantum of change necessary to closely match the AccuRate prediction with the monitored cooling energy use, the temperature set points used in AccuRate for cooling energy evaluation were varied. The results demonstrate that a better match with monthly monitored results is possible from realistic adjustments to the thermostat settings.
Having identified the size of the adjustment necessary, a workshop of nationally recognised thermal comfort experts was held in Adelaide in December 2007 to investigate a theoretical basis for establishing new cooling trigger temperature settings for AccuRate. The expert panel identified two additional aspects of thermal comfort that could be applied to the Chenath cooling methodology and recommended the testing of specific changes.
The results for Adelaide demonstrate that this new methodology and these specific temperature settings provide more realistic values for use in AccuRate cooling energy estimations in comparison with monitoring data.
The study is, however, based on a statistically small sample and highlights the need for extensive national energy consumption data collection and monitoring research. The study identifies that further validation of the cooling aspects of AccuRate will require extensive research into both air conditioner performance and usage patterns during peak demand periods.
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