Center for the Built Environment

Interior space conditioning means heating or cooling building interior space to pro- vide comfort to occupants. In the modern world, the thermostat is a popular form utilized in residential and commercial buildings. Although the thermostat industry has recently matured, the development of new technology provides new opportunities to interior space conditioning. Motivated by the energy crisis, a demand response- enabled interior space conditioning system is designed for residential users. The feature of completely autonomous controls improves the acceptability and usability of the system.

Built on low-cost, low-power wireless technology, the system uses a disaggregated set of sensors and actuators. The software adopts a hierarchical layered structure, providing modularization of functions and semi-independent design. User interfaces provide easy and instructive interaction to users. The system interacts with the public utility, houses and their HVAC systems, users and outside climates. Robust adaptive control is used to address system uncertainties. Validation tools were developed to evaluate the system.

As the major contribution of this research to interior space conditioning, super- visory controls were developed to locate the optimal control settings. Adopting a hierarchical structure, supervisory controls determine control modes, control strate- gies/states, and control settings. To meet users' various requirements on utility cost and thermal comfort, four control strategies/states were designed: the normal strat- egy, the pre-cooling/pre-heating strategy, the pre-conditioning strategy and the over- lapping strategy. The supervisory control strategies were realized by hybrid methods. Expert systems were utilized to choose control mode and control state. Model-based methods or performance-based methods were adopted in each state to seek optimal control settings. Results from computer simulations and field tests indicate that the system responds automatically to price signals with appropriate behavior of energy saving and load shifting. By identifying dynamic signatures of individual houses, the system is able to adapt its control strategies to a house and its HVAC systems as well as to the ever-changing outdoor conditions.

In conclusion, the thesis successfully demonstrates an intelligent, adaptive and autonomous interior space conditioning system under the context of demand response for residential buildings.