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Occupant Plugload Management for Demand Response in Commercial Buildings: Field Experimentation and Statistical Characterization

By Chaitanya Poolla, Abraham K. Ishihara, Dan Liddell, Rodney Martin and Steven Rosenberg

Abstract

Commercial buildings account for approximately 36% of US electricity consumption, of which nearly two-thirds is met by fossil fuels [1] resulting in an adverse impact on the environment. Reducing this impact requires improving energy efficiency and lowering energy consumption. Most existing studies focus on designing methods to regulate and reduce HVAC and lighting energy consumption. However, few studies have focused on the control of occupant plugload energy consumption. In this study, we conducted multiple experiments to analyze changes in occupant plugload energy consumption due to monetary incentives and/or feedback. The experiments were performed in government office and university buildings at NASA Research Park located in Moffett Field, CA. Analysis of the data reveal significant plugload energy reduction can be achieved via feedback and/or incentive mechanisms. Autoregressive models are used to predict expected plugload savings in the presence of exogenous variables. The results of this study suggest that occupant-in-the-loop control architectures have the potential to reduce energy consumption and hence lower the carbon footprint of commercial buildings.Comment: 20 pages, 15 figures, 4 tables, preprin

Topics: Statistics - Applications, Computer Science - Machine Learning, Statistics - Machine Learning
Year: 2020
OAI identifier: oai:arXiv.org:2004.06633

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