Grants and Contracts per year
Grants and Contracts Details
Residential energy consumption has been rapidly increasing during the last decades, especially in the U.S. where 2.6 trillion kilowatt-hours have been consumed during the year 2015 alone. Several research efforts have been made to reduce this consumption, including demand response and smart residential environments. However, previous work in this context has mainly been performed by overlooking the complexity of human nature, and the human actions and perceptions when interacting with such systems. In fact, recent research has shown that these approaches may actually generate negative attitudes, potentially resulting in an increase of the energy consumption and even abandonment and avoidance of such systems. The goal of this proposal is to advance the state of the art in residential energy management by designing algorithmic solutions that learn and take into account human behaviors and perceptions using a variety of tools such as optimization theory, statistics, machine learning and graph theory. This revolutionary approach will allow to reduce the energy consumption of the residential sector and to share renewable energy resources efficiently, while contributing to the NSF big idea “The Future ofWork at the Human-Technology Frontier” by providing methods to improve the human-technology partnership. Intellectual Merit: The proposed research has the potential to transform the way in which energy management for smart residential environments is studied in the field of computer science, by defining and integrating previously unexamined human behaviors and perceptions in the algorithms’ design and optimization techniques. In order to enable fine grained energy monitoring, we propose novel stream-based appliance recognition algorithms for smart outlets. These algorithms not only learn the appliance consumption signatures, but also the user willingness to interact with the system by trading off representatives and informativeness of the collected samples with the learned user behavioral model. Using a smart phone app to collect user feedback developed by undergraduate students as part of their Capstone projects, we design new learning algorithms based on regressograms, interpolation and regression to quantify the user perception of appliances and their dependencies. Using the social-behavioral well-being models that the PI has proposed in his previous interdisciplinary work, we design energy saving optimization strategies that take into account the user perception. In addition, we consider smart residential environments equipped with renewable energy generation and sharing capabilities. We propose optimization algorithms to match users’ demand and production, by taking into account their degree of involvement in the energy exchange process and their preferences. Finally, we address the problem of reproducibility of survey results. In this context, we provide for the first time methodologies to quantify the difference between subject pools that go beyond the standard statistical sampling. The proposed research is validated through real testbeds and large scale simulations based on real traces. The real testbeds are made available through the Power and Energy Institute of Kentucky (PEIK), thanks to collaborations with large regional utilities, such as the Tennessee Valley Authority (TVA) and LGE-KU. Broader Impacts: The proposed research has the potential of expanding the frontiers of residential energy management systems by integrating human behaviors and perceptions in the algorithms design and analysis. Furthermore, the results can benefit society by reducing the energy consumption of the residential sector. In addition, this proposal will have significant impact in the education of elementary, high school, undergraduate and graduate students. Specifically, innovative educational activities supported by this proposal include classes taught by the PI with real time demos, coding challenges and research experiences at the PI lab for students of the Math, Science, and Technology Center (MSTC) program of the Paul Laurence Dunbar High School, Lexington, KY. Furthermore, the PI will be joining the STARS IGNITE program, leading a cohort of students to the diversity-oriented conference Grace Hopper, and teaching periodic seminars for hispanic elementary students by leveraging the University of Kentucky Society of Hispanic Professional Engineers students. This society hosts a weekly seminars called (EFL)2 (Engineering Exploratory for Latinos from Latinos) that focuse on interactive/informational engineering activities with students from Cardinal Valley Elementary School (76% Hispanic demographic). The proposal also provides support for the training of graduate and undergraduate students through exciting research projects, and the produced results will be integrated in the Cyber-Physical-Human Systems class taught by the PI. 1
|Effective start/end date||3/1/20 → 2/28/25|
- National Science Foundation: $540,987.00
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