TY - JOUR KW - Demand response KW - Demand response and distributed energy resources center KW - Demand Response Research Center (DRRC) KW - Peak demand KW - Thermal mass KW - Demand shifting (pre-cooling) KW - DRQAT KW - Auto-dr KW - Demand shed KW - Pre-cooling AU - Rongxin Yin AU - Peng Xu AU - Mary Ann Piette AU - Sila Kiliccote AB -

This paper studies how to optimize pre-cooling strategies for buildings in a hot climate zone of California with the assistance of a building energy simulation tool - Demand Response Quick Assessment Tool (DRQAT). This paper outlines the procedure to develop and calibrate simulation models by using DRQAT, and then applies this procedure into 11 field test buildings. Through comparing the measured demand savings during the peak period with those of simulation model, the results indicate that the predicted demand shed match well with the actual data on Auto-DR days. The study shows that after refining and calibrating initial models with actual measured data, the accuracy of the models can be greatly improved and the models can be used to predict load reductions for automated demand response events. In order to confirm the actual effect of demand response strategies, the simulation results were compared with field test data as well. The results indicated that the optimal demand response strategies worked well for most of buildings in hot climate zone.

BT - Energy and Buildings C2 - LBNL-3541E C5 -

demand response

C6 -

Commercial Building Systems

C7 -

y

DA - 07/2010 ET - January 2010 IS - 7 LA - eng M1 - 7 N1 -

received 7/11/09, revised 1/8/10, accepted 1/9/10

N2 -

This paper studies how to optimize pre-cooling strategies for buildings in a hot climate zone of California with the assistance of a building energy simulation tool - Demand Response Quick Assessment Tool (DRQAT). This paper outlines the procedure to develop and calibrate simulation models by using DRQAT, and then applies this procedure into 11 field test buildings. Through comparing the measured demand savings during the peak period with those of simulation model, the results indicate that the predicted demand shed match well with the actual data on Auto-DR days. The study shows that after refining and calibrating initial models with actual measured data, the accuracy of the models can be greatly improved and the models can be used to predict load reductions for automated demand response events. In order to confirm the actual effect of demand response strategies, the simulation results were compared with field test data as well. The results indicated that the optimal demand response strategies worked well for most of buildings in hot climate zone.

PY - 2010 SP - 967 EP - 975 T2 - Energy and Buildings TI - Study on Auto-DR and Pre-cooling of Commercial Buildings with Thermal Mass in California VL - 42 ER -