ELEKTROENERGETIKA |
VESTI DOGAĐAJI |
BAZA EE EE BLOG |
FIRME FAKULTETI |
KNJIGE ČASOPISI |
POSLOVI LINKOVI |
|||
KNJIGE -
Pretraživanje: "grid"
D,S. Kirschen - Power Systems: Fundamental Concepts and the Transition to Sustainability O.D. Doleski - Handbook of Electrical Power Systems N. Mohan - Electric Power Systems with Renewables I. Kerszenbaum - Handbook of Large Turbo-Generator Operation and Maintenance T. Letcher - Storing Energy T. L. Burton - Wind Energy Handbook S.A. Roosa - Fundamentals of Microgrids A. Apostolov - IEC 61850: Digitizing the Electric Power Grid A. Keyhani - Design of Smart Power Grid Renewable Energy Systems S.S. Refaat - Smart Grid and Enabling Technologies SLEDEĆA >> Prikaz knjiga od 1 do 10 od ukupno 104 rezultata pretrazivanja |
R. Arghandeh - Big Data Application in Power Systems Big Data Application in Power Systems 1st Edition by Reza Arghandeh, Yuxun Zhou No. of pages: 480 Language: English Copyright: © Elsevier Science 2017 Published: November 27, 2017 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. Key Features 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 About the Editors 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. Table of Contents SECTION 1 Harness the Big Data From Power Systems 1. A Holistic Approach to Becoming a Data-Driven Utility John D. McDonald, GE Energy Connections-Grid Solutions, Atlanta, GA, United States 2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions Carol L. Stimmel, Manifest Mind, LLC, Canaan, NY, United States 3. The Role of Big Data and Analytics in Utility Innovation Jeffrey S. Katz, IBM, Hartford, CT, United States 4. Frameworks for Big Data Integration, Warehousing, and Analytics Feng Gao, Tsinghua University Energy Internet Research Institute, Beijing, China SECTION 2 Harness the Power of Big data 5. Moving Toward Agile Machine Learning for Data Analytics in Power Systems Yuxun Zhou, and Reza Arghandeh, UC Berkeley and Florida State University, Tallahassee, FL, United States 6. Unsupervised Learning Methods for Power System Data Analysis Thierry Zufferey*, Andreas Ulbig*†, Stephan Koch*†, Gabriela Hug* ETH Zurich, Power Systems Laboratory, Zurich, Switzerland, Adaptricity AG, c/o ETH Zurich, Power Systems Laboratory, Zurich, Switzerland 7. Deep Learning for Power System Data Analysis Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Eindhoven University of Technology, Eindhoven, The Netherlands 8. Compressive Sensing for Power System Data Analysis Mohammad Babakmehr*, Mehrdad Majidi†, Marcelo G. Simoes* Colorado School of Mines, Golden, CO, United States University of Nevada, Reno, NV, United States 9. Time-Series Classification Methods: Review and Applications to Power Systems Data Gian Antonio Susto, Angelo Cenedese, Matteo Terzi, University of Padova, Padova, Italy SECTION 3 Put the Power of Big Data into Power Systems 10. Future Trends for Big Data Application in Power Systems Ricardo J. Bessa, INESC Technology and Science—INESC TEC, Porto, Portugal 11. On Data-Driven Approaches for Demand Response Akin Tascikaraoglu, Mugla Sitki Kocman University, Mugla, Turkey 12. Topology Learning in Radial Distribution Grids Deepjyoti Deka, Michael Chertkov, Los Alamos National Laboratory, Los Alamos, NM, United States 13. Grid Topology Identification via Distributed Statistical Hypothesis Testing Saverio Bolognani, Automatic Control Laboratory ETH Zurich, Zurich, Switzerland 14. Supervised Learning-Based Fault Location in Power Grids Hanif Livani, University of Nevada Reno, Reno, NV, United States 15. Data-Driven Voltage Unbalance Analysis in Power Distribution Networks Matthias Stifter*, Ingo Nader AIT Austrian Institute of Technology, Center of Energy, Vienna, Austria Unbelievable Machine, Vienna, Austria 16. Predictive Analytics for Comprehensive Energy Systems State Estimation Yingchen Zhang*, Rui Yang*, Jie Zhang†, Yang Weng‡, Bri-Mathias Hodge* National Renewable Energy Laboratory, Golden, CO, United States University of Texas at Dallas, Richardson, TX, United States Arizona State University, Tempe, AZ, United States 17. Data Analytics for Energy Disaggregation: Methods and Applications Behzad Najafi, Sadaf Moaveninejad, Fabio Rinaldi, Polytechnic University of Milan, Milan, Italy 18. Energy Disaggregation and the Utility-Privacy Tradeoff Roy Dong*, Lillian J. Ratliff† University of California, Berkeley, Berkeley, CA, United States University of Washington, Seattle, WA, United States |