Energy in Communication, Information, and Cyber-physical Systems (E6) - Keynote Speech

Keynote speaker

Prashant Shenoy , University of Massachusetts, Amherst, USA


Prashant Shenoy is currently a Professor of Computer Science at the University of Massachusetts Amherst. He received the B.Tech degree in Computer Science and Engineering from the Indian Institute of Technology, Bombay and the M.S and Ph.D degrees in Computer Science from the University of Texas, Austin. His research interests lie in distributed systems and networking, with a recent emphasis on cloud and green computing. He has been the recipient of the National Science Foundation Career Award, the IBM Faculty Development Award, the Lilly Foundation Teaching Fellowship, and the UT Computer Science Best Dissertation Award, and several best paper awards at leading conferences. He serves on editorial boards of the ACM Transactions on the Web and the Multimedia Systems journal and has served as the past program chair for ACM Multimedia, ACM Sigmetrics, World Wide Web, Performance, Multimedia Computing and Networking, and Hotcloud 2009. He is a distinguished member of the ACM and a senior member of the IEEE.

Talk title:  Demand-side Load Management in Smart Homes

Abstract:  Studies have shown that buildings consumer over 75% of the total
electricity and over 45% of the total energy usage in many countries. Given rising energy prices and climate change, energy efficiency techniques that reduce the energy and carbon footprint of buildings is an important societal need. In this talk, I will argue that Information and Communication technologies (ICT)  are a crucial component for achieving energy efficiency in smart buildings.  I will describe demand-side load management techniques where users or  intelligent building components take an active role in reducing and optimizing a building's energy footprint and also present present hurdles in achieving energy-efficiency through these methods. Finally,  I will describe two of our recent efforts in demand-side load management via peak load shaving. Our first effort focuses on background load scheduling and coordination to transparently reduce peak electricity load without any change in user behavior, while our second technique uses small batteries and any available local renewable sources to shift supply to demand, rather than conventional techniques that adjust demand based on supply.




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