Abstract
Smart home is an emerging area that opens up a diverse set of downstream applications, such as dynamic pricing and demand response techniques, whose goals are typically to lower power consumption while providing comfortable and convenient services. Smart home has been extensively studied, and shown to be beneficial in people's real lives. Although useful, typical smart home platforms work at the servant level -- highly dependent on user inputs with no predictions of human demands -- which, if well addressed, would significantly widen their applicability. In this article, we propose a human-centric smart home energy management system (SHE) that works at the level. The system integrates ubiquitous sensing data from the physical and cyber spaces to discover the patterns of power usage and cognitively understand the behaviors of human beings. The relationship between them is established to dynamically infer users' demands for electricity, and then the optimal scheduling of the home energy system is triggered to respond to both the users' demands and electricity rates. Based on the novel framework, our SHE system provides intelligent services to satisfy the requirements of users as a butler -- aiming not only to save the electricity cost or reduce the peak load, but also to predict users' demands and managing servants.
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