DEVELOPMENT OF METHODS FOR DETERMINING DEMAND-LIMITING SETPOINT TRAJECTORIES IN COMMERCIAL BUILDINGS USING SHORT-TERM DATA ANALYSIS
Kyoung-ho Lee, James E. Braun
This paper presents simple approaches for estimating building zone temperature set-point variations that minimize peak cooling demand during critical demand periods. Three different methods were developed that are termed the semi-analytical (SA), exponential setpoint equation-based semi-analytical (ESA), and load weighted-averaging (WA) methods. The three methods are different in terms of requirements for input data and ability to reduce peak demand. The SA and ESA methods employ simple inverse building models trained with short-term data and use analytical solutions from the models to determine setpoint trajectories. The WA method is a data-based method in which an optimal weighing factor is found that minimizes a weighted- average of two loads. In addition to determining setpoint trajectories, the methods provide estimates of peak load reduction. A companion paper (Lee and Braun 2006b) presents evaluations of the peak load reduction potential associated with implementation of these methods.
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