传统的冷却策略在很大程度上忽略了建筑结构的热容量。仿真和实验表明，当使用控制策略对建筑质量进行预冷时，可以实现显著的运行节约。这些节约源于较低的公用事业费率和夜间设备性能的改善。测试还表明，控制策略必须与应用程序相匹配，才能实现这些节约。有效的控制策略可以通过模拟建筑和使用优化技术来开发，以最小化运营成本。不幸的是，这对于大多数应用程序来说并不实用。通过检查覆盖范围广泛的建筑物、冷却设备、天气、利用率和内部增益的最佳冷却结果，开发了简化方法，显著降低了最优控制问题的维数。本文提出了两种简化方法，每种方法在建筑物无人居住时采用两个控制变量，并结合占用期间的一组规则。 这些规则是以乘客舒适度为标准的。与传统控制相比，简化后的优化节省分别达到95%和97%。使用每小时的时间步长，每日优化问题从24个变量减少到2个变量。除了减少研究最优控制和开发特定于建筑物的控制策略的计算需求外，这些简化还可用于在线控制器的开发。还开发了一种简化的夜间通风预冷控制策略。该算法由一个考虑预冷效率的简化代价函数导出。通过每日和季节性模拟，证明了夜间设置控制的节省。对于广泛的系统和操作条件，通风预冷算法可以实现89%的通风预冷可能的最佳每日节约。 与夜间设置控制下的制冷成本相比，该算法显示季节性节约高达32%。节省的资金在很大程度上取决于地理位置和建筑施工。在夏季干冷的气候条件下，重型建筑的性能最好。在夜间设置控制中，高时段电价倍增器提供了最高的潜在节约，然而，该算法在没有时段电价的应用中实现了节约。还计算了乘员舒适度，发现其与夜间设置控制下获得的舒适度水平相当。

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.