Reliable Non-Parametric Techniques for Energy System Operation and Control : Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods (Advances in Intelligent Energy Systems)

個数：

予約

Reliable Non-Parametric Techniques for Energy System Operation and Control : Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods (Advances in Intelligent Energy Systems)

Zhang, Hongcai/ Song, Yonghua/ Chen, Ge

ウェブストア価格 ¥39,536（本体¥35,942）
Elsevier - Health Sciences Division（2025/07発売）
外貨定価 US$ 195.00
ポイント 359pt

現在予約受付中です。出版後の入荷・発送となります。
（重要：表示されている発売日は予定となり、発売が延期、中止、生産限定品で商品確保ができないなどの理由により、ご注文をお取消しさせていただく場合がございます。予めご了承ください。）

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 350 p.
言語 ENG
商品コード 9780443364921
DDC分類 621.31

Full Description

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. Introduction

PART I: ENERGY SYSTEM OPERATION BASED ON CONSTRAINT LEARNING
2. Fundamentals of Constraint Learning and Its Application in Deterministic Energy System Operation Problems
3. Extending Constraint Learning to Energy System Operations under Uncertain Environments
4. Ensuring Accuracy of Constraint learning in the Face of Imbalanced Operational Datasets
5. Overcoming Measurement Limitations by Combining Constraint Learning with Measurement Recovery
6. Mathematical Insights and Computationally-efficient Implementations of Constraint Learning

PART II: ENERGY SYSTEM CONTROL BASED ON SAFE-REINFORCEMENT LEARNING
7. Training-efficient Intrinsic-motived Reinforcement Learning Control for Energy Systems with Soft Operation Constraint
8. Physical Layer-based Safe Reinforcement Learning Control for Energy Systems with Accurate Formula of Hard Operation Constraint
9. Barrier Function-based Safe Reinforcement Learning Control for Energy Systems with Partially Formulable Hard Operation Constraint
10. CVaR-based Safe Reinforcement Learning Control for Energy Systems without Formula of Hard Operation Constraint
11. Conclusion