IEEE Transactions on Emerging Topics in Computing
 

Human Behavior Aware Energy Management in Residential Cyber-Physical Systems

Baris Aksanli Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 
Tajana Simunic Rosing Department of Computer Science and Engineering, University of California, San Diego, CA 
 

Technological advancements, such as smart appliances, have enabled residential buildings to become a true Cyber-Physical System (CPS), where the devices correspond to the physical system and the smart computation and control mechanisms define the cyber part. An important aspect of these residential cyber-physical systems is their large portion of the overall energy consumption in the electric grid. Researchers have proposed several methods to address the issue, targeting to reduce both the consumption and the cost associated with it, either individually or simultaneously. These methods include using renewable energy sources, energy storage devices, efficient control methods to maximize the benefits of these resources, and smart appliance rescheduling. However, a residential CPS, different than a common CPS, has a lot of direct human interaction within the system. Although the previous residential energy management methods are effective, they do not consider the inherent and dominant human factor. This paper develops a human-behavior-centric smart appliance rescheduling method for a residential neighborhood. We first show an accurate representation of the relationship between the activities of the household members and the power demand of the house. We use this model to efficiently generate several power profiles based on different household characteristics. Then, we formally model how flexible users are when rescheduling appliances. In contrast to previous studies, our work is able to capture the intrinsic human behavior related decisions and actions when automating the residential energy consumption. Our results show 16 percent energy savings and 22 percent reduction in peak power relative to the case without appliance rescheduling while accurately representing and meeting human-related constraints. We also demonstrate that ignoring human preferences can lead to up to more than 90 percent violation of user deadlines.


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