Big Data Application in Power Systems

Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down the big picture for electric utilities before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies.

Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today’s challenges in this rapidly accelerating area of power engineering.
Readers will develop new strategies and techniques for leveraging data towards real-world outcomes.

• Provides a total refresh to include the most up-to-date research, developments, and challenges
• Focuses on practical techniques, including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal data
• Engages with cross-disciplinary lessons, drawing on the impact of intersectional technology including statistics, computer science, and bioinformatics
• Includes five brand new chapters on hot topics, ranging from uncertainty decision-making to features, selection methods, and the opportunities provided by social network data

Prof. Reza Arghandeh is the Director of Connectivity, Information & Intelligence Lab (Ci2Lab.com) and a Full Professor in Data Science and Machine Learning in the Department of Computer Science, Electrical Engineering, and Mathematical Sciences at the Western Norway University of Applied Sciences (HVL), Bergen, Norway. He is also the HVL Data Science Group (HVL.no/ai). Additionally, he is a Research Professor in the Electrical and Computer Department at Florida State University, USA, where he was an assistant professor from 2015 to 2018. Prior to FSU, he was a postdoctoral scholar at the University of California, Berkeley, EECS Dept 2013-2015. His research interests include data analysis and decision support for smart grids and smart cities. His research has been supported by IBM, the U.S. National Science Foundation, the U.S. Department of Energy, the European Space Agency, the European Commission, and the Research Council of Norway.

Yuxun Zhou received his B.S. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 2009, the Diplome d’Ingénieur degree in applied mathematics from École Centrale Paris, Paris, France, in 2012, and a Ph.D. degree from the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA, in 2017. He has been an author on over 60 research articles and conference proceedings published in peer-reviewed journals. Dr Zhou’s research interests include statistical learning theory and paradigms for modern information-rich, large-scale, and human-involved systems.