Guest Editors
Prof. Muhammad Faheem, University of Vaasa, Finland
Prof. Piyush Kumar Shukla, State Technological University of Madh, India
Dr. Rashid Ali, University West, Sweden
Summary
Blockchain technology provides a robust, transparent, and decentralized framework for handling energy transactions and data within the smart grid infrastructure. Concurrently, machine learning facilitates advanced predictive analysis, real-time system optimization, and informed decision-making processes. This special edition is dedicated to uniting eminent scholars and researchers from the realms of blockchain, machine learning, and energy systems. It’s aim is to explore the potential of these technologies in tackling the intricate challenges faced by contemporary energy systems. This publication will prove to be an invaluable asset for researchers, industry practitioners, policy makers, and professionals in the field, interested in the convergence of blockchain and machine learning for smart grid applications. It will contribute to the advancement of more effective, secure, and sustainable energy solutions.
Within the domain of smart grid technology, blockchain serves as a decentralized and immutable ledger system, ensuring transparency and security in transaction management. This framework enables trustless interactions between various grid systems and devices, maintaining the integrity of energy transactions. It plays a pivotal role in facilitating peer-to-peer energy trading and enhancing grid management strategies. In contrast, machine learning harnesses the power of data analytics and sophisticated algorithms to accurately forecast energy demand, streamline grid operations, and foster proactive maintenance approaches. The integration of blockchain's robust data management with the predictive prowess of machine learning equips smart grids with the capability to garner real-time insights and automate decision-making processes. This amalgamation significantly contributes to the development of a resilient and environmentally sustainable energy infrastructure. The convergence of these technologies marks a paradigm shift in the generation, distribution, and consumption of electricity, leading to heightened efficiency and sustainability within the energy sector. We welcome a diverse range of contributions that delve into various aspects of this field, including, but not limited to:
1. Blockchain-based energy trading and peer-to-peer transactions within the smart grid.
2. Machine learning for demand forecasting, load management, and predictive maintenance in the energy sector.
3. Decentralized and secure data management solutions for smart grid applications.
4. Integration of renewable energy sources and electric vehicles through blockchain and machine learning.
5. Cybersecurity considerations for blockchain-based smart grid applications.
6. Microgrid management and optimization with machine learning and blockchain.
7. Advanced experimental/testing methods using AI in energy systems.
We encourage submissions of original research articles, reviews, case studies, and technical notes that provide insights into the potential, challenges, and real-world implementations of blockchain and machine learning in the smart grid. By compiling the latest research and innovations in this field, we aim to facilitate a deeper understanding of the opportunities and limitations of these technologies in shaping the future of sustainable and intelligent energy systems.
Keywords
Cybersecurity, Blockchain, Machine Learning, Internet of Energy Things, Smart Grid
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