AI and Blockchain in Smart Grids: Fundamentals, Methods, and Applications examines the cutting-edge solution that combines artificial intelligence (AI), blockchain technology, and digital twin concepts to innovate the management and optimization of electrical power distribution. This innovative approach enhances the resilience, efficiency, and security of electricity grids while providing real-time insights for grid operators and stakeholders. The book covers such key elements as using:
- Digital twins in smart grids to gather real-time data from various grid components
- AI-powered analytics to process the data generated by digital twins and to analyze this information to detect patterns, predict grid failures, and recommend adjustments to enhance a grid's performance
- Blockchain-based security to ensure the secure and transparent management of data within a smart grid, especially a tamper-resistant ledger to store information related to energy production, distribution, and consumption
- Decentralized data sharing to allow grid data to be shared securely among various stakeholders, including utilities, regulators, and consumers
- Grid optimization techniques to improve electricity distribution, reduce energy waste, and balance supply and demand efficiently
Select real-world case studies and practical examples demonstrate how AI and blockchain are currently being applied to enhance grid management, energy distribution, and sustainability. By explaining to researchers, academics, and students how AI and blockchain can revolutionize electricity distribution and make grids smarter, more secure, and environmentally friendly, the book points to a future where grid operators, regulators, and consumers will benefit from real-time data and a resilient, efficient energy ecosystem.
The book discusses emerging technologies driving the transformation of energy grids. It focuses on the integration of AI and blockchain to create digital twins that will provide data-driven insights into smart grid operations. It explores how these technologies optimize energy distribution, grid resilience, and efficient management.
1 Machine Learning Fundamentals 2 Machine Learning and Predictive Maintenance in Smart Grids
3. Machine Learning, Deep Learning and Internet of Things-based Smart Grids and Power Systems
4. Integration of AI and Blockchain with Digital Twins for Smart Grid
5. Project WattNext: Decoding for Tomorrow's Energy Demands
6. A Blockchain-Based Authentication Framework for Secure Data Sharing
7. Security and Privacy Issues in AI: Blockchain-Enabled Digital Twin-Based Smart Grid
8. AI and Blockchain Applications in Smart Grids and the Energy Sector
9. Leading AI Applications in The Sustainable Energy Sector
10. Unveiling the World with Precision: A Journey into Semantic Segmentation
11. A Blockchain-Based Authentication Framework for Secure Data Sharing
12. Node Identification Algorithm in Cluster-Based Fog Computing in VANET Using Normal Distribution
13. Analysis of FOREX Forecasting Using Machine Learning and Deep Learning Techniques
14. Energy Management Approaches Using Artificial Intelligence and Blockchain 15 An Improved Object Detection Algorithm for Maritime Search and Rescue based on Drone Imagery
16. Blockchain Controlled Offline IoT Data Stream Secured Using Identity-Based Proxy Re-Encryption Technique
17. Analysis of AI For Optimization of Smart Grids 18 Powering Up e-Health: AI and Blockchain for a Smarter and Safer e-Health System 19 A Blockchain-Based Architecture for Secure and Decentralized Agricultural Supply Chain Management 20. A Vital Research and Modernization Real Time Practice in Artificial Intelligence in Smart Grid Environment
21. An Intelligent Digital Twin Framework for Condition Monitoring of Aircraft
Amit Kumar Tyagi is an assistant professor, Department of Fashion Technology, National Institute of Fashion Technology, New Delhi. He earned a PhD degree from Pondicherry Central University, India. He has worked as an assistant professor and senior researcher at the School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. He is a regular member of the ACM and senior member of IEEE.
Shrikant Tiwari received a PhD degree from the Department of Computer Science & Engineering at the Indian Institute of Technology (Banaras Hindu University), Varanasi, India. Currently, he is an associate professor in the School of Computing Science and Engineering (SCSE), Galgotias University, Greater Noida, India. He has authored or co-authored more than 50 national and international journal publications, book chapters, and conference articles. He has five patents filed to his credit. His research interests include machine learning, deep learning, computer vision, medical image analysis, pattern recognition, and biometrics. Dr. Tiwari is a FIETE, a senior member of the IEEE, and member of ACM, IET, CSI, ISTE, IAENG, and SCIEI.
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