Intelligent Learning Approaches for Renewable and Sustainable Energy 1st Edition

Capture the future of clean power with Intelligent Learning Approaches for Renewable and Sustainable Energy, 1st Edition by Josep M. Guerrero, Pankaj Gupta, Ritu Kandari, and Alexander Micallef. This authoritative work brings together cutting-edge machine learning and data-driven techniques tailored to modern renewable energy systems, offering a clear path from theory to practical application.

Explore how intelligent learning transforms design, control, and operation of solar, wind, and storage assets. The authors translate complex concepts—deep learning, reinforcement learning, predictive analytics, and adaptive control—into accessible explanations with real-world relevance. Readers will discover approaches for forecasting generation and demand, enhancing grid stability, optimizing energy management, and enabling smarter microgrids and distributed energy resources.

Ideal for researchers, graduate students, power engineers, and policymakers, this book balances rigorous methodology with actionable insight. Case studies and application-focused discussions highlight solutions for contemporary challenges across European, Asian, and North American energy markets, making it relevant to global practitioners seeking sustainable, scalable strategies.

Whether you are developing intelligent control for smart grids, improving renewable integration, or advancing R&D in sustainable energy, this edition equips you with the analytical tools and practical perspective to make an impact. Written in a professional yet engaging style, it’s an essential resource for navigating the intersection of AI and renewable energy.

Bring advanced, data-driven solutions to your projects and research—add Intelligent Learning Approaches for Renewable and Sustainable Energy, 1st Edition to your library today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.