Residential energy consumption has been rising rapidly during the last few decades. Several research effortshave been made to reduce residential energy consumption, including demand response and smart residentialenvironments. However, recent research has shown that these approaches may actually cause an increase inthe overall consumption, due to the complex psychological processes that occur when human users interactwith these energy management systems. In this article, using an interdisciplinary approach, we introduce aperceived-value driven framework for energy management in smart residential environments that considershow users perceive values of different appliances and how the use of some appliances are contingent on theuse of others.We define a perceived-value user utility used as an Integer Linear Programming (ILP) problem.We show that the problem is NP-Hard and provide a heuristic method called COndensed DependencY (CODY).We validate our results using synthetic and real datasets, large-scale online experiments, and a real-fieldexperiment at the Missouri University of Science and Technology Solar Village. Simulation results showthat our approach achieves near optimal performance and significantly outperforms previously proposedsolutions. Results from our online and real-field experiments also show that users largely prefer our solutioncompared to a previous approach.
|Number of pages||26|
|Journal||ACM TRANSACTIONS ON THE INTERNET OF THINGS|
|Publication status||Published - 2020|