Solar Energy Optimization Using Generative Artificial Intelligence

Lead the sustainable energy revolution with this guide to mastering the AI-driven algorithms and smart material innovations that are revolutionizing solar energy.

The integration of artificial intelligence into solar energy systems represents the next frontier in sustainable development, promising to improve efficiency, reduce costs, and increase the viability of solar energy as a mainstream energy source. This book will delve into the transformative role of artificial intelligence in enhancing various aspects of solar energy systems. It will begin by exploring how AI can significantly boost the energy efficiency of solar panels, showcasing innovative algorithms and techniques designed to optimize energy capture and conversion. The development of smart materials for enhanced energy storage will also be covered, emphasizing the latest advancements in material science driven by AI to improve the storage capabilities and longevity of solar panels. Further, it will address integrated waste management options for exhausted solar panels, providing insights into sustainable practices and AI-driven solutions for recycling and repurposing solar panel components. It will discuss the significance of AI in solar energy conservation and climate change management, illustrating how AI technologies are being harnessed to predict, monitor, and mitigate environmental impacts. Additionally, the book will explore the future scope of photovoltaic-based solar energy in a changing environment, highlighting AI’s role in achieving sustainability and adapting to evolving climatic conditions. Using case studies and real-world applications, this book will demonstrate successful implementations of AI in the solar energy sector. Topics such as predictive maintenance, solar irradiance forecasting, optimal placement of solar panels, and AI-enhanced solar tracking systems will be featured to provide a comprehensive understanding of how AI is revolutionizing the solar energy landscape.

Abhishek Kumar, PhD is the Research and Design Coordinator and an Associate Professor in the Department of Computer Science at Chandigarh University. He has more than 100 publications in reputed, peer-reviewed national and international journals, books, and conferences. His research areas include artificial intelligence, image processing, computer vision, data mining, and machine learning.

Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University. With more than 11 years of academic teaching experience, he has published more than 55 papers in reputable, peer-reviewed national and international journals, books, and conferences, and co-authored and edited numerous books. His research interests include NS2, computer networks, mining, and database management systems.

Arun Lal Srivastav, PhD is an Associate Professor at Chitkara University. He has published more than 90 research publications in prestigious journals, books, and conferences, edited 23 books, and filed 25 patents. His research interests include energy management, water quality surveillance, climate change, and water treatment.

Ashutosh Kumar Dubey, PhD is a Postdoctoral Fellow at the Ingenium Research Group Lab at the Universidad de Castilla-La Mancha with more than 14 years of teaching experience. He has authored one book and serves as an editor and editorial board member of many peer-reviewed journals. His research areas are machine learning, renewable energy, cloud computing, data mining, health informatics, optimization, and object-oriented programming.