Data science develops strong algorithms that can process large amounts of information quickly and efficiently, increase security and privacy of sensitive data and provide an avenue for informed decision-making and proper planning. This is crucial for ever-evolving large power systems like the electric grid.
The gathering and analysis of generated data has remained a key component for the operation and planning of the power grid since its inception. However, over the last decade, the convergence of massive amounts of new data sets, an abundance of advanced computing capabilities and tremendous progress in machine learning technologies that propelled the rapid development of data science in power systems has significantly increased the need to incorporate big data ideologies into the field.
Having access to this data and harnessing these data science principles helps us to run the grid more reliably, efficiently and sustainably.
Over the last several years, Dr. Le Xie, professor in the Department of Electrical and Computer Engineering and associate director of energy digitization at the Texas A&M Energy Institute, has worked to provide a data-driven perspective to graduate students in the department and industry professionals for the operation and planning of the electric grid.
“Having access to this data and harnessing these data science principles helps us to run the grid more reliably, efficiently and sustainably,” Xie said.
Xie, who is also Segers Family Dean’s Excellence Professor, Chancellor EDGES Fellow and Presidential Impact Fellow at Texas A&M University, has served in various capacities supporting the effort to integrate data science into power systems, including serving as the founding chair of the Institute of Electrical and Electronics Engineers Power and Energy Society’s technical subcommittee on big data and analytics. He has developed courses at Texas A&M in this area and recently published a textbook on the topic.