A Data-Driven Method for 1 Determining Zone Temperature 2 Trajectories that Minimize Peak 3 Electrical Demand

10 Lee and Braun (2007b) developed a simple approach, termed the weighted-averaging (WA) method, 11 that estimates building zone temperature set-point variations that minimize peak cooling demand during 12 critical demand periods. The current paper extends this approach to air conditioning power demand and 13 presents the results of a field evaluation for a small commercial building. The WA method uses data from 14 two or more test days to determine a near-optimal setpoint trajectory for minimizing peak demand during a 15 demand-limiting period. Evaluation of the method was performed over a two-week test period at a small 16 bank building located in Palm Desert, California. The first week of testing was used to collect baseline data 17 for conventional control and data for estimating the optimal setpoint trajectory. The second week of testing 18 was used to evaluate demand reduction for application of trajectories determined using the WA method. 19 INTRODUCTION 20 Thermal storage in building thermal mass can be controlled using adjustment of building space 21 setpoint temperatures to reduce peak cooling load and power demand. In conventional night-setup (NS) 22 control, zone setpoint temperature is maintained constant during occupied periods and set up during 23 unoccupied periods. In demand-limiting control, the building is precooled prior to an on-peak period and 24 then setpoints are modulated in a way so that the thermal storage in the building thermal mass is controlled 25 to reduce peak electrical demand. 26 There have been a number of simulation and experimental studies that have demonstrated significant 27 potential for reducing peak cooling demand using building thermal mass (Braun 1990, Rabl and Norford 28 1991, Andresen and Brandemuehl 1992, Braun 2003, Morris et al. 1994, Keeney and Braun 1997, Braun et 29 al. 2001, Braun and Lee 2006, Lee and Braun 2006, and Xu and Haves 2006). However, there has been 30 less work on the development of practical control methods for minimizing peak demand. Braun and Lee 31 (2006) found that a simple exponential form for zone temperature setpoint variation during on-peak periods 32 can be effective in reducing peak cooling demand. They used a simulation tool and estimated electrical 33 demand reduction of 1 to 2 W per square foot of floor space for a 4-hour demand-limiting period in small 34 commercial buildings in California climates. However, it is necessary to determine building-specific time 35 constants. 36 Lee and Braun (2007a) suggested a model-based demand-limiting method that relies on a trained 37 inverse building model. The inverse building model was trained using data from a small test building 38 located in Iowa and the method was validated experimentally at the building. The test results showed 30% 39 reductions in peak cooling loads with setpoint adjustments from 70 to 76°F (21.1 to 24.4C) for a 5-hour 40 demand-limiting period. The results were consistent with simulation results that were determined for this 41 facility. 42 Lee and Braun (2007b, 2007c) developed three simplified methods for determining demand-limiting 43 setpoint trajectories using short-term measurements, which are termed the semi-analytical (SA) method, the 44 exponential setpoint equation-based SA (ESA) method, and the weighted-averaging method (WA method). 45 The SA and ESA methods employ simplified inverse building models trained with short-term data and use 46