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This report presents results from field testing and comfort surveys designed to evaluate peak demand-limiting strategies that utilize both precooling and adjustments of zone cooling setpoints. The testing was performed over a two-week period at a small bank building in Palm Desert, California. During the first week test, three kinds of control strategies were considered: 1) conventional night setup control as a baseline case, 2) a simple linear-rise demand-limiting strategy that involved precooling during the morning and linear setpoint adjustments during an afternoon demand-limiting period, and 3) a simple step-up demand-limiting strategy that included precooling in the morning and resetting of setpoint during the demand-limiting period.During the second week of testing, a demand-limiting strategy was tested for four days with setpoint trajectories determined using a weighted-averaging method developed at Purdue University. Precooling of the building was performed at 70ºF setpoint from 6am to 12pm and setpoints during the on-peak period from 12pm to 6pm were modulated from 70 to 78ºF following a trajectory that attempted to minimize peak cooling load. (The measured temperature at the polling station was a minimum of 1.5 degrees F above the thermostat setpoint (see figures 24 to 29 in appendix). The baseline was conventional night-setup control with a 72ºF cooling setpoint temperature during the occupied period. The demand-limiting tests resulted in greater than 30% reduction of peak air conditioner power on average for the four tested days which accounted for 0.76W/ft2 peak savings. The comfort survey revealed that the response of occupants was highly variable at any given indoor temperature. Statistical analysis of all the data collected, including baseline days and test days, indicated a significant probability that a given occupant will vote that the temperature is ‘cool’ at the low setpoint temperature of 70 degrees (between 30 and 50 percent), and ‘warm’ at the upper setpoint of 78 degrees (between 37 and 52 percent) (figure 21). However, only half of these votes are at the level where the ii respondent says it ‘bothers’ them. The probability of a given occupant being bothered by the ‘cool’ temperature at the low setpoint is estimated to be 17 percent and the probability of a given occupant being bothered by the ‘warm’ temperature at at the upper setpoint is estimated to be 23 percent. (figure 22). If we assume the neutral temperature to be between 74 and 75 degrees F, the probability of dissatisfaction (both ‘Too warm! It bothers me’ and ‘Too cool! It bothers me’) is estimated to be 20 percent.

Most commercial buildings have been dedicated HVAC systems to meet their comfort needs. In larger commercial buildings, the control of this equipment is achieved through use of computerized control systems, which allow the flexibility of running the building under different control strategies. Most control systems vary the building temperatures using a strategy called night-setup control. Under night-setup control, the air temperatures are in the middle of the comfort range and the cooling equipment is usually active during daytime hours. During the night and weekends, a high temperature setpoint is sent out by the control system and the cooling system is typically inactive during these periods. However, recently there has been an increased emphasis on developing dynamic building control strategies, which attempt to minimize the total cooling costs of a building and reduce peak cooling demand. Accurate building modeling tools are needed for predicting the building thermal loads under different control strategies. The work described in this report involved development and evaluation of models using data from the Energy Resource Station (ERS) at the Iowa Energy Center (IEC). Models trained with data from the ERS were used to estimate peak cooling load reduction associated with different demand-limiting and precooling strategies. The IEC is a relatively lightweight structure that is representative of small commercial buildings. Demand reduction results for this building would be smaller than those possible for larger commercial buildings. The specific work tasks that provided the basis for the results described in this report are summarized as follows: A. Develop forward simulation model for the ERS building B. Develop inverse simulation model for the ERS building C. Evaluate demand-limiting control strategies

The literature shows five decades of interest in vapor compression cooling for spacecraft, while only very few conclusions can be drawn because of either non-systematic approaches or sparsely documented experiments. In contrast, the demand for high COP refrigerators and freezers has increased with the emerging plans of long duration manned space travel. Research is needed exploiting all available testing approaches to investigate two-phase cycles in microgravity environments. This paper presents relevant testing possibilities with their characteristics and outlines open questions regarding vapor compression cycles in space. Beneficial experiments are derived from open questions and matched with available testing methods to prescribe a path towards reliable and efficient refrigeration systems in microgravity.