Call for Chapters: Explainable Artificial Intelligence and Solar Energy Integration

Editors

Jay Kumar Pandey, Shri Ramswaroop Memorial University, India

Call for Chapters

Proposals Submission Deadline: July 21, 2024
Full Chapters Due: September 22, 2024
Submission Date: September 22, 2024

Introduction

The book "Explainable Artificial Intelligence and Solar Energy Integration" explores the intersection of two transformative fields: renewable energy and artificial intelligence. With a primary focus on solar energy integration and the utilization of Explainable Artificial Intelligence (XAI), this book aims to provide a comprehensive understanding of how cutting-edge technologies can be leveraged to optimize power system management and foster sustainable development. The subject area of the book encompasses renewable energy technologies, particularly solar energy, and the role of artificial intelligence, specifically XAI, in addressing the challenges associated with its integration into existing power systems. It delves into topics such as grid stability, energy forecasting, optimization, and decision support, offering insights into how XAI techniques can enhance efficiency, reliability, and sustainability in energy systems. The book's aims and scope are multifaceted. Firstly, it aims to bridge the gap between renewable energy research and artificial intelligence applications, offering interdisciplinary insights into the synergies between these fields. Secondly, it seeks to provide practical guidance for researchers, professionals, policymakers, and students interested in advancing renewable energy integration and AI-driven solutions for sustainable development. Thirdly, it aims to stimulate dialogue and collaboration among stakeholders in the renewable energy and AI communities, fostering innovation and knowledge exchange. The reasons for writing this book are twofold. Firstly, there is a growing urgency to transition towards renewable energy sources to mitigate climate change and address energy security concerns. Secondly, the rapid advancements in artificial intelligence offer unprecedented opportunities to optimize energy systems and accelerate the transition to a sustainable future. By combining these two domains, the book aims to contribute to the collective efforts towards achieving a more resilient, efficient, and environmentally friendly energy infrastructure. Special design features and novel approaches employed in the book include: Case studies and real-world examples illustrating the application of XAI in solar energy integration and power system management. Interviews with industry experts, researchers, and policymakers providing firsthand insights and perspectives on the challenges and opportunities in the field. Educational resources such as glossaries, references, and supplementary materials to support readers in understanding complex concepts and terminology. Ethical and social considerations addressing the responsible deployment of AI technologies in critical infrastructure and the implications for equity, transparency, and privacy. Overall, the book offers a holistic exploration of the subject area, combining technical insights with practical applications and ethical considerations to provide a comprehensive resource for anyone interested in advancing solar energy integration and sustainable energy futures through the lens of Explainable Artificial Intelligence.

Objective

The objective of the book "Explainable Artificial Intelligence and Solar Energy Integration" is to bridge the gap between advanced AI methodologies and practical applications in the solar energy sector by providing a comprehensive guide to the implementation and benefits of XAI. The book aims to enhance the understanding of how AI-driven systems can be made transparent and interpretable, thereby fostering greater trust and adoption among engineers, decision-makers, and stakeholders in the solar energy industry. It seeks to contribute to current research by offering detailed insights into the techniques and tools of XAI, such as feature importance, LIME, Shapley values, and saliency maps, and demonstrating their practical applications in optimizing solar energy production, fault detection, energy management, and grid integration. By some studies, empirical research, and theoretical frameworks, the book intends to advance the field by: 1. Promoting Trust and Transparency: Illustrating how XAI can make AI models more understandable, thereby increasing trust among users and regulatory bodies. 2. Enhancing Reliability and Performance: Showing how XAI can be used to diagnose and correct errors, leading to more reliable and efficient solar energy systems. 3. Facilitating Regulatory Compliance: Providing methods for ensuring AI systems comply with energy sector regulations through transparent decision-making processes. 4. Driving Innovation: Encouraging the development of new XAI techniques and their application in solar energy, pushing the boundaries of current research. Overall, the book aims to be an essential resource for researchers, practitioners, and policymakers, helping to integrate AI transparently and effectively into solar energy systems, thereby advancing both fields simultaneously.

Target Audience

The book "Explainable Artificial Intelligence and Solar Energy Integration" is geared towards a diverse audience, including: 1. Researchers and Academics: Scholars in the fields of artificial intelligence, renewable energy, and electrical engineering will find the book valuable for its in-depth analysis of XAI techniques and their applications in solar energy. It offers a solid foundation for academic research, encouraging further studies and innovation in the integration of AI with renewable energy systems. 2. Engineers and Technical Professionals: Practitioners working in the solar energy sector, including system designers, engineers, and technicians, will benefit from the practical insights and case studies that demonstrate how XAI can optimize solar energy systems, enhance reliability, and improve fault detection and diagnosis. 3. Energy Sector Decision-Makers and Policymakers: Individuals involved in energy policy, regulation, and decision-making will find the book useful for understanding how XAI can ensure compliance with regulatory standards, increase transparency, and build trust in AI-driven energy systems. It provides a framework for evaluating and implementing AI solutions in a regulated environment. 4. Business Leaders and Industry Stakeholders: Executives and managers in the renewable energy industry can leverage the book’s insights to make informed decisions about adopting and investing in AI technologies for solar energy. It helps them understand the benefits and challenges of integrating XAI, leading to better strategic planning and resource allocation. 5. Students and Educators: The book serves as an educational resource for students studying AI, renewable energy, or related fields, and for educators looking to incorporate cutting-edge research and practical applications into their curriculum. It provides foundational knowledge and real-world examples that enhance learning and teaching. By catering to these audiences, the book aims to facilitate the adoption of explainable AI in solar energy, driving advancements in technology, improving system performance, and promoting sustainable energy practices.

Recommended Topics

1. Introduction to Overview of Solar Energy Integration, Importance of XAI in Power System Management & Aims and Scope of the Book 2. Solar Energy Integration: Challenges and Opportunities, Status of Solar Energy Adoption: Challenges in Solar Energy Integration & Opportunities for Advancement 3. Introduction to Explainable Artificial Intelligence (XAI), Basics of Artificial Intelligence: Need for XAI in Critical Domains 4. Power System Management and Grid Integration, Overview of Power System Control: Role of Renewable Energy in Power Systems & Challenges in Grid Integration of Solar Energy 5. XAI Techniques for Power System Optimization, Interpretable Machine Learning Models: Causal Reasoning and Decision Support Systems & Anomaly Detection and Fault Diagnosis 6. Applications of XAI in Solar Energy Integration, Solar Energy Forecasting and Resource Assessment: Grid Operation and Demand-Side Management & Energy Storage Optimization and Grid Resilience 7. Case Studies and Real-World Examples, XAI-Driven Solar Forecasting System 8. Ethical and Social Implications, Ethical Considerations in AI Deployment: Social Implications of Renewable Energy Integration Equity, Transparency, and Privacy Concerns 9. Policy and Regulatory Frameworks, Government Initiatives and Incentives: Regulations for Renewable Energy Deployment & Guidelines for Responsible AI Adoption 10. Future Directions and Emerging Trends, Technological Innovations in Solar Energy Integration: Research Challenges and Opportunities & Collaborative Approaches for Sustainable Energy Futures 11. Conclusion and Call to Action: Summary of Key Findings & Recommendations for Future Action to the Importance of Collaboration and Knowledge Exchange

Submission Procedure

Researchers and practitioners are invited to submit on or before July 21, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by August 4, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by September 22, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Explainable Artificial Intelligence and Solar Energy Integration. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

July 21, 2024: Proposal Submission Deadline
August 4, 2024: Notification of Acceptance
September 22, 2024: Full Chapter Submission
October 27, 2024: Review Results Returned
November 24, 2024: Final Acceptance Notification
December 1, 2024: Final Chapter Submission



Inquiries

Dr. Jay Kumar Pandey Department of Electrical & Electronics Engineering , Shri Ramswaroop Memorial University, Lucknow Deva Road, Barabanki, U.P, India er.jay11@gmail.com

Classifications


Computer Science and Information Technology; Education; Media and Communications; Government and Law; Physical Sciences and Engineering
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