Artificial intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, reshaping industries, economies, and the way societies address complex global challenges. Among its most promising applications is its ability to accelerate the transition to renewable energy and support more sustainable development. As the world confronts climate change, rising energy demands, and the need to reduce carbon emissions, intelligent technologies are playing an increasingly important role in optimizing energy production, improving system efficiency, and supporting smarter decision-making. AI offers new opportunities to enhance the reliability and resilience of renewable energy systems while enabling more effective management of energy resources. Understanding how these technologies can drive a cleaner and more sustainable energy future has become a critical priority for researchers, industry leaders, and policymakers alike. Sustaining Intelligent Societies Through Renewable Energy Sources for AI Development explores how AI can enhance and optimize renewable energy systems to build more resilient and sustainable societies. Rather than viewing AI as a burden on energy infrastructure, this book positions it as a transformative enabler capable of addressing the intermittent and variable nature of renewable energy, improving energy forecasting, advancing smart grid management, and driving innovation in energy storage. Covering topics such as thermal optimization, geothermal technologies, and hydrogen energy systems, this book is a fundamental academic resource for graduate and doctoral students, energy engineers, utility professionals, renewable energy specialists, smart grid researchers, infrastructure planners, technology professionals, sustainability professionals, environmental planners, policymakers, and more.
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Dr. Manar Alkhatib is an Assistant Professor of Artificial Intelligence at the British University in Dubai (BUiD). Her research focuses on advancing Arabic NLP, including developing models for Arabic text processing such as sentiment analysis, machine translation, and grammatical error correction. She applies deep learning and machine learning techniques to improve language understanding and processing, with a particular interest in the computational linguistics challenges of the Arabic language.
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