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Conventional cooling strategies largely ignore the thermal capacitance of the building structure. Simulations and experiments have shown that significant operating savings can be realized when control strategies are used that precool the mass of the building. These savings result from lower utility rates and improved equipment performance at nighttime. Testing has also shown that the control strategy must be matched to the application to achieve these savings. Effective control strategies can be developed by simulating the building and using optimization techniques to minimize the operating costs. Unfortunately, this is not practical for most applications.

By examining optimal cooling results covering a wide range of buildings, cooling plants, weather, utility rates, and internal gains, simplifications were developed which significantly reduced the dimensionality of the optimal control problem. Two simplified approaches are presented which each employ two control variables while the building is unoccupied in conjunction with a set of rules for the occupied period. The rules were expressed in terms of occupant comfort. The simplifications achieved 95% and 97%, respectively, of the optimal savings relative to conventional control. Using hourly time-steps, the daily optimization problem was reduced from 24 to 2 variables. In addition to reducing the computational requirements to study optimal control and develop building specific control strategies, these simplifications could be used in the development of an on-line controller.

A simplified control strategy for precooling with night ventilation was also developed. The algorithm was derived from a simplified cost function which considers the efficiency of precooling. The savings over night setup control were demonstrated using both daily and seasonal simulations. For a wide range of systems and operating conditions, the ventilation precooling algorithm was found to achieve 89% of the optimal daily savings possible with ventilation precooling. The algorithm showed seasonal savings of up to 32% when compared to the cooling cost under night setup control. The savings were highly dependent on geographical location and building construction. The best performance was found to occur in heavy construction buildings located in climates characterized by dry cool summers. High time-of-day electric rate multipliers provided the highest potential savings over night setup control, however the algorithm resulted in savings in applications with no time-of-day utility rates. Occupant comfort was also calculated and found to be comparable to comfort levels achieved under night setup control.