Big Data Application in Power Systems

Häftad, Engelska, 2017

1 339 kr

Beställningsvara. Skickas inom 7-10 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids.

Provides expert analysis of the latest developments by global authorities
Contains detailed references for further reading and extended research
Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics
Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Produktinformation

Utgivningsdatum2017-11-27
Mått191 x 235 x undefined mm
Vikt930 g
FormatHäftad
SpråkEngelska
Antal sidor480
FörlagElsevier Science
ISBN9780128119686

Tillhör följande kategorier

Energiteknik inom Naturvetenskap och teknik

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.

SECTION 1 Harness the Big Data From Power Systems1. A Holistic Approach to Becoming a Data-Driven Utility2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions3. The Role of Big Data and Analytics in Utility Innovation4. Frameworks for Big Data Integration, Warehousing, and Analytics SECTION 2 Harness the Power of Big data5. Moving Toward Agile Machine Learning for Data Analytics in Power Systems6. Unsupervised Learning Methods for Power System Data Analysis7. Deep Learning for Power System Data Analysis8. Compressive Sensing for Power System Data Analysis 9. Time-Series Classification Methods: Review and Applications to Power Systems DataSECTION 3 Put the Power of Big Data into Power Systems10. Future Trends for Big Data Application in Power Systems11. On Data-Driven Approaches for Demand Response12. Topology Learning in Radial Distribution Grids13. Grid Topology Identification via Distributed Statistical Hypothesis Testing14. Supervised Learning-Based Fault Location in Power Grids15. Data-Driven Voltage Unbalance Analysis in Power Distribution Networks16. Predictive Analytics for Comprehensive Energy Systems State Estimation17. Data Analytics for Energy Disaggregation: Methods and Applications18. Energy Disaggregation and the Utility-Privacy Tradeoff