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The renewable energy sector is undergoing rapid transformation, driven by technological innovation, evolving regulations, and growing demand for sustainable energy solutions. While resources exist on individual aspects of renewable energy, few provide a comprehensive guide that integrates science, business strategy, and data-driven modeling in one cohesive resource. Roadmap to Renewable Energy: Integrating Science, Business, and AI is meant to fill this gap, offering a holistic approach for professionals, students, and policymakers navigating the evolving energy landscape. The book combines real-world examples, case studies, and practical applications to equip readers with the knowledge and strategies needed to plan, deploy, and manage renewable energy projects effectively. Readers will gain hands-on experience through accessible code notebooks, enabling them to explore predictive models, analyze data, and apply insights to real-world energy scenarios. AIdriven modeling is woven throughout, providing actionable insights for project forecasting, optimization, and strategic decision-making.
List of contents
Introduction to renewable energy fusion.- The science of renewable energy.- Policy and regulatory frameworks for renewable energy.- Environmental and social considerations.- Technological innovations in renewable energy.- Financial modeling and economic analysis.- Market analysis and business strategies.- Analytical techniques for optimization.- Project management and implementation.- Future trends and opportunities in renewable energy.- Case studies of successful renewable energy fusion.- Conclusion and final thoughts.
About the author
Disha Gupta is a renewable energy expert who has led the development of utility-scale solar, wind, and battery storage projects across the United States. She holds a bachelor’s degree in Earth Science from the University of Delhi, a master’s in Geology and Geological Engineering from South Dakota School of Mines & Technology (SDSM&T), and an MBA. Disha began her career as a GIS analyst at Energiekontor US, advanced to wind energy projects at BayWa r.e. in California, and has contributed to renewable energy development with EDP Renewables North America. Recognized for her academic excellence in The Times of India, she was also featured in AP News and South Dakota Capitol Journal for her group research at SDSM&T. An AI enthusiast, she will begin a master’s in computer science at Georgia Tech in Spring 2026 to further broaden her technical expertise.
Daniel J. Soeder has 45 years of experience as a research scientist and geologist working on issues related to energy and the environment. His background includes a decade of research on the geology of natural gas resources at the Gas Technology Institute in Chicago, followed by 18 years with the U.S. Geological Survey (USGS) coordinating hydrologic and geologic fieldwork at the proposed Yucca Mountain high level nuclear waste repository site in Nevada, and researching coastal hydrology, wetlands, water supply, and water contamination in the Mid- Atlantic. He transferred from the USGS to the U.S. Department of Energy (DOE) National Energy Technology Laboratory in Morgantown, West Virginia in 2009 where he spent eight years performing energy and environmental research on gas shale and other unconventional fossil energy resources. He took an early retirement from the government to direct the Energy Resources Initiative at the South Dakota School of Mines & Technology.
Aditya Chichani is a senior machine learning engineer at Walmart, with expertise in building production-grade ML models and software applications. At Walmart Search, he designs end-to-end ML solutions, leveraging deep knowledge in machine learning and information retrieval to improve attribute understanding and ranking, delivering accurate search results to millions of shoppers at the world’s largest retailer. Previously, he developed scalable microservices for Barclaycard Germany and key clients, including Amazon. He holds a master’s degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley, specializing in Data Science. Alongside his industry contribution, Aditya has organized workshops and served on program committees at leading AI/ML conferences, including ACM SIGIR, IEEE ICDM, CIKM, and RecSys. He has received several honors, including Excellence Awards at Walmart and Barclays, and the prestigious Fung Excellence Scholarship at UC Berkeley.
Summary
The renewable energy sector is undergoing rapid transformation, driven by technological innovation, evolving regulations, and growing demand for sustainable energy solutions. While resources exist on individual aspects of renewable energy, few provide a comprehensive guide that integrates science, business strategy, and data-driven modeling in one cohesive resource. Roadmap to Renewable Energy: Integrating Science, Business, and AI is meant to fill this gap, offering a holistic approach for professionals, students, and policymakers navigating the evolving energy landscape. The book combines real-world examples, case studies, and practical applications to equip readers with the knowledge and strategies needed to plan, deploy, and manage renewable energy projects effectively. Readers will gain hands-on experience through accessible code notebooks, enabling them to explore predictive models, analyze data, and apply insights to real-world energy scenarios. AIdriven modeling is woven throughout, providing actionable insights for project forecasting, optimization, and strategic decision-making.
