Guest Editors
Prof. Dr. S. B. Goyal
Email: shyam.goyal@chitkara.edu.in
Affiliation: Department of Computer Science & Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
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Research Interests: smart energy management, big-data information, system artificial intelligence, block chain, cloud computing
Prof. Dr. Darpan Anand
Email: darpan.anand@chitkara.edu.in
Affiliation: Department of Computer Science & Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
Homepage:
Research Interests: smart grids, energy management, information security, block chain, machine learning, e-governance, cryptography
Dr. Chaman Verma
Email: chaman@inf.elte.hu
Affiliation: Department of Media and Educational Technology, Faculty of Informatics (Ik), Eötvös Loránd University, Budapest, Hungary
Homepage:
Research Interests: data analytics, IoT, energy management, feature engineering, real-time systems, educational informatics
Dr. Divya Prakash Shrivastava
Email: dp_shrivastava@yahoo.com
Affiliation: Zayed University, Abu Dhabi, United Arab Emirates
Homepage:
Research Interests: big data, AI, machine learning, web technology, object oriented analysis and design
Dr. Leo S.F. Lin
Email: slin@csu.edu.au
Affiliation: Charles Sturt University, Bathurst, Australia
Homepage:
Research Interests: AI-enabled management, big data, emerging technologies, security management
Summary
The fast development of artificial intelligence (AI), Internet of Things (IoT), and blockchain technologies is changing the current energy systems to intelligent, adaptive, and data-driven ecosystems. Nonetheless, the growing sophistication of AI-based decision-making prompts the issue of transparency, trust, and interpretability, particularly in vital infrastructure, e.g., smart grids and energy management systems. The proposed Special Issue will be focused on how Explainable AI (XAI) and intelligent computational structures can be integrated into sustainable energy systems to guarantee energy generation, distribution, and consumption transparency, reliability, and efficiency. The problem statement will be based on state-of-the-art research in the area of challenges and advances in AI-based energy systems, highlighting the interpretability of AI, optimization, security, and sustainability.
The adoption of Artificial Intelligence (AI) in modern energy systems has contributed to a great extent in improving the efficiency, automation, and intelligence of energy generation, distribution, and use. Nevertheless, the growing trend of utilizing sophisticated AI systems within the critical energy infrastructure, including smart grids, creates significant issues of transparency, trust, interpretability, and security. In that regard, the Explainable Artificial Intelligence (XAI) has become an indispensable concept to ensure that decisions made by AI can be explained, reliable, and acceptable to stakeholders.
It aims at an alliance of interdisciplinary studies that tackle the issues of XAI integration with smart grids, renewable energy systems, IoT-facilitated energy networks, and blockchain-facilitated energy transactions.
The Special Issue invites contributions that create explainable AI models, hybrid intelligent systems, and human-friendly solutions to the energy systems. It focuses on applications that increase energy forecasting, optimization, demand response, cybersecurity, and sustainability analytics, and promotes fairness, accountability, and compliance with regulations. The Special Issue will help in filling the gap between the sophisticated AI technologies and the realistic energy consumption by proposing solutions toward the establishment of reliable, safe, and sustainable smart energy ecosystems.
Some of the Key Topics Include (but are not limited to):
- The Explainable AI (XAI) in Smart Grids and Energy Systems.
- Energy Consumption Prediction and Load Forecasting based on AI.
- Renewable Energy Optimization using Explainable AI.
- Secure Energy Transactions using blockchain.
- Edge AI and IoT in Energy Monitoring and Control.
- AI/ XAI for Demand Response and Energy Efficiency.
- Smart grid intelligence Hybrid AI Models.
- AI Energy-based Systems for Trust, Security, and Cybersecurity in An Energy System.
- Energy Management for Digital Twins and Simulation.
- Sustainable and Green Energy Analytics using AI.
- XAI in Human-Centric Energy Systems.
- Ethical and regulatory issues & mechanism of XAI in Energy.
Keywords
explainable artificial intelligence (XAI), trustworthy AI, energy efficiency, energy forecasting, energy optimization, machine learning, renewable energy, blockchain in energy, IoT in energy, energy analytics, artificial intelligence, sustainable energy, digital twins, cybersecurity in energy systems, human-centric AI
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