Big Data Application in Power Systems 1st Edition – Ebook PDF Instant Delivery – ISBN(s): 9780128119686,0128119683,9780128119693, 0128119691
Product details:
- ISBN-10 : 9780128119686
- ISBN-13 : 978-0128119686
- Author: Reza Arghandeh, Yuxun Zhou
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.
Table contents:
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
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
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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