Browsing by Subject "building energy simulation"
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Item Expanding the applicability of residential economizers through HVAC control strategies(2009-12) Kaufman, David E.; Siegel, Jeffrey A.; Novoselac, AtilaThis study seeks to expand the range of climates and conditions in which free cooling from an economizer can replace air conditioning power consumption in residential applications. To explore this issue, we first discretize a simple building model in space and in time. We then solve the associated energy and mass balances for the estimated hourly heating and cooling loads and humidity conditions with respect to an annual climate profile. We propose a forecast-based algorithm to control the rate of outdoor airflow brought in by an economizer, in response to the upcoming cooling load to be experienced by the interior airspace. The algorithm takes advantage of a range of acceptable temperatures for thermal comfort by precooling the envelope overnight to delay the onset of cooling demand during the day. In order to consider the highest potential benefit from such an algorithm, we bypass the considerable problem of forecast accuracy by basing the inputs on the upcoming cooling load according to an initial simulation of the full year. On the whole, even with the forecast-based control, the results of the study have much in common with previous findings in the literature. Precooling works better to reduce cooling load in cases of higher thermal and moisture mass, but a humid climate severely restricts when free cooling is beneficial. For the example house considered here with the Austin climate and other assumptions, the effect of the proposed forecast-based economizer control was to greatly reduce the indoor air cooling load while greatly increasing the number of annual hours of unacceptably high indoor humidity. When we adjusted the forecast-based algorithm to avoid the excess humidity, the remaining reduction in cooling load was not significant. To investigate further how a forecast-based economizer could reduce cooling load in humid climates, the prinicipal task should be to extend the control algorithm to forecast and manage upcoming indoor humidity levels in the same fashion as was done in this study for indoor air temperature.