Smart Grids and Power Conservation

Residential energy consumption has been rapidly increasing during the last decades. For instance, in the U.S. 2.6 trillion kilowatt-hours were consumed during 2015, and an additional 13.5% increase is expected by 2040. On the other hand, blackouts from weather-related events have increased by 67% since 2000. A significant number of research studies on power conservation and demand response have focused on techniques to cope with peak loads that often are the cause of such blackouts and power outages. However, current studies have largely overlooked the complexity of human behaviors and perceptions, failing to target the need of individual users, as well as realistic individual power consumption. The objective of this proposal is to overcome the limitations associated with state-of-the-art energy management systems by designing novel algorithms, machine learning models, and optimization techniques that specifically consider user behaviors, perceptions, and psychological processes. This revolutionary approach will unleash the full potential of smart residential environments in reducing residential energy consumption and has the potential to transform the way in which energy management systems are designed, implemented, and used by people. The proposed research combines novel algorithmic, machine learning, and optimization solutions that consider previously un-examined human behaviors, perceptions, and psychological processes.