Reliable Non-Parametric Techniques for Energy System Operation and Control: Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods offers a comprehensive guide to cutting-edge smart methods in energy system operation and control. This book begins by covering fundamentals, applications in deterministic and uncertain environments, accuracy in imbalanced datasets, and overcoming measurement limitations. It also delves into mathematical insights and computationally-efficient implementations. Part II addresses energy system control using safe reinforcement learning, exploring training-efficient intrinsic-motivated reinforcement learning, physical layer-based control, barrier function-based control, and CVaR-based control for systems without hard operation constraints. Designed for graduate students, researchers, and engineers, Reliable Non-Parametric Techniques for Energy System Operation and Control stands out for its practical approach to advanced methods in energy system control, enabling sustainable developments in real-world conditions.

• Bridges the gap between theory and practice, providing essential insights for graduate students, researchers, and engineers
• Includes visual elements, data and code, and case studies for easy understanding and implementation
• Part of the Series Advances in Intelligent Energy Systems, bringing together the latest innovations in smart, sustainable energy