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Reliable Non-Parametric Techniques for Energy System Operation and Control

Teacher Information

Name
SONG Yonghua ZJU Faculty Homepage
Department
INTL Campus
Research
Power System Operation and Optimization, Demand-Side Response, Urban Energy Systems, and Electricity Policy and Markets
Document Type
Book
Reliable Non-Parametric Techniques for Energy System Operation and Control

Publication

Subtitle
Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods
Author/Author of Source
Hongcai Zhang, Yonghua Song,Ge Chen and Peipei Yu
Publisher
Elsevier
P-ISBN
9789819910922
E-ISBN
9789819910908
Abstract

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.

DOI
https://doi.org/10.1016/C2024-0-02424-1

Print & Electronic Holdings

Database
ScienceDirect
Link