Reliable Non-Parametric Techniques for Energy System Operation and Control: Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods, a new Volume in the Advances in Intelligent Energy Systems, is a comprehensive guide to modern smart methods in energy system operation and control. This book covers fundamental concepts and applications in both deterministic and uncertain environments. It addresses the challenge of accuracy in imbalanced datasets and the limitations of measurements. The book delves into advanced topics such as safe reinforcement learning for energy system control, including training-efficient intrinsic-motivated reinforcement learning, and physical layer-based control, and more.
Other chapters cover barrier function-based control and CVaR-based control for systems without hard operation constraints. Designed for graduate students, researchers, and engineers, this book stands out for its practical approach to advanced methods in energy system control, enabling sustainable developments in real-world conditions.
1. Chapter One: Introduction - p. 1 2. Part One: Energy system operation based on constraint learning - p. 13 3. Chapter Two: Fundamentals of constraint learning and its application in deterministic energy system operation problems - p. 17 4. Chapter Three: Extending constraint learning to energy system operations under uncertain environments - p. 43 5. Chapter Four: Ensuring accuracy of constraint learning in the face of imbalanced operational datasets - p. 75 6. Chapter 5: Overcoming measurement limitations by combining constraint learning with measurement recovery - p. 105 7. Chapter Six: Mathematical insights and computationally-efficient implementations of constraint learning - p. 139 8. Part Two: Energy system control based on safe-reinforcement learning - p. 165 9. Chapter Seven: Training-efficient intrinsic-motived reinforcement learning control for energy systems with soft operation constraint - p. 169 10. Chapter Eight: Physical layer-based safe reinforcement learning control for energy systems with accurate formula of hard operation constraints - p. 197 11. Chapter Nine: Barrier function-based safe reinforcement learning control for energy systems with partially formulable hard operation constraint - p. 231 12. Chapter Ten: CVaR-based safe reinforcement learning control for energy systems without formula of hard operation constraints - p. 257 13. Chapter Eleven: Conclusions - p. 285 14. Index - p. 289
Reliable Non-Parametric Techniques for Energy System Operation and Control: Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods, a new Volume in the Advances in Intelligent Energy Systems, is a comprehensive guide to modern smart methods in energy system operation and control. This book covers fundamental concepts and applications in both deterministic and uncertain environments. It addresses the challenge of accuracy in imbalanced datasets and the limitations of measurements. The book delves into advanced topics such as safe reinforcement learning for energy system control, including training-efficient intrinsic-motivated reinforcement learning, and physical layer-based control, and more.
Other chapters cover barrier function-based control and CVaR-based control for systems without hard operation constraints. Designed for graduate students, researchers, and engineers, this book stands out for its practical approach to advanced methods in energy system control, enabling sustainable developments in real-world conditions.
1. Chapter One: Introduction - p. 1 2. Part One: Energy system operation based on constraint learning - p. 13 3. Chapter Two: Fundamentals of constraint learning and its application in deterministic energy system operation problems - p. 17 4. Chapter Three: Extending constraint learning to energy system operations under uncertain environments - p. 43 5. Chapter Four: Ensuring accuracy of constraint learning in the face of imbalanced operational datasets - p. 75 6. Chapter 5: Overcoming measurement limitations by combining constraint learning with measurement recovery - p. 105 7. Chapter Six: Mathematical insights and computationally-efficient implementations of constraint learning - p. 139 8. Part Two: Energy system control based on safe-reinforcement learning - p. 165 9. Chapter Seven: Training-efficient intrinsic-motived reinforcement learning control for energy systems with soft operation constraint - p. 169 10. Chapter Eight: Physical layer-based safe reinforcement learning control for energy systems with accurate formula of hard operation constraints - p. 197 11. Chapter Nine: Barrier function-based safe reinforcement learning control for energy systems with partially formulable hard operation constraint - p. 231 12. Chapter Ten: CVaR-based safe reinforcement learning control for energy systems without formula of hard operation constraints - p. 257 13. Chapter Eleven: Conclusions - p. 285 14. Index - p. 289
