Abstract
This paper discusses how to optimize pre-cooling strategies for buildings in a hot California climate zone with the Demand Response Quick Assessment Tool (DRQAT), a building energy simulation tool. This paper outlines the procedure used to develop and calibrate DRQAT simulation models, and applies this procedure to eleven field test buildings. The results of a comparison between the measured demand savings during the peak period and the savings predicted by the simulation model indicate that the predicted demand shed match well with measured data for the corresponding auto-demand response (Auto-DR) days. The study shows that the accuracy of the simulation models is greatly improved after calibrating the initial models with measured data. These improved models can be used to predict load reductions for automated demand response events. The simulation results were compared with field test data to confirm the actual effect of demand response strategies. Results indicate that the optimal demand response strategies worked well for most of the buildings tested in this hot climate zone.
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