TitleData Predictive Control for Energy CPS

Where: Alan Turing building (formerly, Coppito 0). Room A1.6 (first floor).
When: Tuesday 12 July, 1o:00 am
Speakers: Achin Jain (PhD student @UPENN University – Philadelphia)

AbstractDecisions on how best to optimize today’s energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive. We present data-driven methods for making control-oriented models for demand response and peak power reduction in buildings. Specifically, we propose an algorithm for data predictive control with regression trees (DPCRT). DPCRT is a finite receding horizon method, using which the building operator can optimally trade off power consumption against thermal comfort without having to learn white/grey box models of the systems dynamics.

Bio: Achin Jain is a doctoral student in Electrical and Systems Engineering at the University of Pennsylvania. He received M.Sc. in Robotics, Systems and Control from ETH Zurich, Switzerland in 2015 and B.Tech. in Mechanical Engineering from the Indian Institute of Technology Delhi, India in 2012. His research interests include controls, optimization, statistics, machine learning applied to cyber-physical systems. His current research focuses on methods for predictive control with data-driven models.

Seminar:”Data Predictive Control for Energy CPS”

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