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e on Earth and Beyond (series) e on Earth and Beyond (series)

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Author:   Reza Arghandeh (Assistant Prof. in Electrical Engineering, Florida State University) ,  Yuxun Zhou (Ph.D candidate, Department of EECS, UC Berkeley)
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Elsevier Science Publishing Co Inc
Weight:   0.930kg
ISBN:  

9780128119686

ISBN 10:   0128119683
Pages:   480
Publication Date:   27 November 2017
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

SECTION 1 Harness the Big Data From Power Systems 1. A Holistic Approach to Becoming a Data-Driven Utility 2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions 3. The Role of Big Data and Analytics in Utility Innovation 4. Frameworks for Big Data Integration, Warehousing, and Analytics SECTION 2 Harness the Power of Big data 5. Moving Toward Agile Machine Learning for Data Analytics in Power Systems 6. Unsupervised Learning Methods for Power System Data Analysis 7. Deep Learning for Power System Data Analysis 8. Compressive Sensing for Power System Data Analysis 9. Time-Series Classification Methods: Review and Applications to Power Systems Data SECTION 3 Put the Power of Big Data into Power Systems 10. Future Trends for Big Data Application in Power Systems 11. On Data-Driven Approaches for Demand Response 12. Topology Learning in Radial Distribution Grids 13. Grid Topology Identification via Distributed Statistical Hypothesis Testing 14. Supervised Learning-Based Fault Location in Power Grids 15. Data-Driven Voltage Unbalance Analysis in Power Distribution Networks 16. Predictive Analytics for Comprehensive Energy Systems State Estimation 17. Data Analytics for Energy Disaggregation: Methods and Applications 18. Energy Disaggregation and the Utility-Privacy Tradeoff

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Author Information

Reza Arghandeh - Assistant Prof. in Electrical Engineering, Florida State UniversityContributed to the Elsevier publication Renewable Energy Integration: Practical Management of Variability, Uncertainty and Flexibility and has published more than 20 journal papers related to smart grid technologies, monitoring systems, data analysis for control, and diagnostic application in power systems.Research InterestsDistributed Control, Data Analysis, Modeling & Simulation Tools for Power System/Power Electronics Diagnostics and Operation applications including but not limited to: Distributed Energy Resources Integration; Buildings, Mircogrids and Distribution Management Systems (BMS-DMS); Grid Automation; Adaptive Demand Response; Electric Grid Observability; Distribution Network Model Validation; Smart Grid Cyber-Physical Resilience; and Internet of Power Electronic Inverters. Yuxun Zhou is currently a Ph.D candidate at Department of EECS, UC Berkeley. Prior to that, he obtained the Diplome d'Ingenieur in Applied Mathematics from Ecole Centrale Paris, and a B.S. degree from Xi'an Jiaotong University. Yuxun has published more than 20 refereed articles, and has received several student awards. His research interest is on machine learning and control methods for modern sensor rich, ubiquitously connected energy systems, including smart buildings, power distribution networks, power systems with renewable integration, etc. Among others, he has designed novel algorithms for non-convex machine learning problems, and has constructed a series of event detection methods for complex systems with resource and information constraints.

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