Addressing new methodologies in deep learning (DL), machine learning (ML) and artificial intelligence (AI), the webinar speakers will provide an overview of the literature spanning these three overlapping fields as applied to energy systems research. The audience will learn how developments in these areas have added new capabilities for pattern recognition and predictive modeling that are complementary to more traditional modeling approaches used in energy systems research. The speakers will illustrate several use cases of these new approaches in the energy space, such as physics-guided neural networks to improve ML regressions for solar data and how AI agents can be used to explore power plant operations.
Anna (Ebers) Broughel is a renewable energy economist at an engineering consulting firm Tetra Tech. Her most recent research reviewed over 60 international energy scenarios for 2040 and beyond, developing four main scenarios for post-COVID19 energy futures. Prior to joining Tetra Tech, she worked at the U.S. Department of Energy as a Science and Technology Fellow. During her post-doctoral training at the University of St.Gallen in Switzerland and at the University of Maryland, College Park, she researched social acceptance of wind energy and other energy technologies. She holds a PhD in economics and policy from the State University of New York in association with Syracuse University, where she was a Fulbright scholar. Currently, she serves as a council member for the U.S. Association for Energy Economics and is a non-resident fellow at the University of Texas at Austin. In the past, Dr. Broughel has taught classes in energy policy and climate change as an adjunct professor at the University of Maryland, College Park.
David Broadstock is Deputy Director of the Center for Economic Sustainability and Entrepreneurial Finance and an Advisor to the Accounting and Finance Tech-Lab at The Hong Kong Polytechnic University. David specializes in applied econometrics of energy and the environment, with a particular emphasis on topics in consumer behaviour and energy finance. Currently, David serves as IAEE Vice President for conferences and is an Editor for The Energy Journal.
Varun Rai is an Associate Professor in the LBJ School of Public Affairs and in the Department of Mechanical Engineering at the University of Texas at Austin, where he directs the Energy Systems Transformation Research Group (aka "Rai Group"). His interdisciplinary research - delving with issues at the interface of energy systems, complex systems, decision science, and public policy - focuses on studying how the interactions between the underlying social, behavioral, economic, technological, and institutional components of the energy system impact the diffusion of energy technologies. Over the last 15 years, his research has applied various analytical lenses to study technologies and policies in carbon capture and storage (CCS), fuels cells, oil & gas, plug-in hybrid vehicles (PEVs), and solar photovoltaics (PV). He has presented at several important forums, including the United States Senate Briefings, Global Intelligent Utility Network Coalition, and Global Economic Symposium, and his research group's work has been discussed in The New York Times, The Wall Street Journal, Washington Post, and Bloomberg News, among other venues. He was a Global Economic Fellow in 2009 and holds the Elspeth Rostow Centennial Fellowship at the LBJ School. During 2013-2015 he was a Commissioner for the vertically-integrated electric utility Austin Energy. In 2016 the Association for Public Policy Analysis & Management (APPAM) awarded him the David N. Kershaw Award and Prize, which "was established to honor persons who, at under the age of 40, have made a distinguished contribution to the field of public policy analysis and management." He received The Eyes of Texas Excellence Award, also in 2016, for making "noteworthy contributions to the UT community." Dr. Rai has held the position of the Associate Dean for Research for the LBJ School since September 2017. He received his Ph.D. and MS in Mechanical Engineering from Stanford University and a bachelor's degree in Mechanical Engineering from the Indian Institute of Technology (IIT) Kharagpur.
Grant Buster is a data engineer at the National Renewable Energy Laboratory (NREL). His current research applies machine learning and artificial intelligence to energy applications around the world. His work focuses on how to use our understanding of physics and physical phenomena to help guide the development of data-driven algorithms. Before working at NREL, Grant worked as a nuclear risk analyst at NuScale Power, the nation's leading engineering company for next-generation small modular reactor technology. With degrees in mechanical and nuclear engineering from the University of California at Berkeley, Grant has a wide range of research interests from theory-guided data science to advanced nuclear reactor designs.