Physics-Aware Machine Learning for Integrated Energy Systems Management

Mohammadreza Daneshvar, Behnam Mohammadi-Ivatloo, Kazem Zare, Jamshid Aghaei

• Publisher: Elsevier
• Year: 2025
• Topic: Technology & Engineering
• eBook ISBN:
  9780443329852
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

Physics-Aware Machine Learning for Integrated Energy Systems Management, a new release in the Advances in Intelligent Energy Systems series, guides the reader through this state-of-the-art approach to computational methods, from data input and training to application opportunities in integrated energy systems. The book begins by establishing the principles, design, and needs of integrated energy systems in the modern sustainable grid before moving into assessing aspects such as sustainability, energy storage, and physical-economic models. Detailed, step-by-step procedures for utilizing a variety of physics-aware machine learning models are provided, including reinforcement learning, feature learning, and neural networks.Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, this book is a holistic introduction to an exciting new approach in energy systems management. - Outlines the challenges, opportunities, and applications for utilizing physics-aware machine learning to support renewable energy integration to the modern grid - Covers a wide variety of techniques, from fundamental principles to security concerns - Represents the latest offering in the cutting-edge series, Advances in Intelligent Energy Systems, which introduces these essential multidisciplinary skills to modern energy engineers

Table of contents

• Title of Book
• Chapter 1 Attributes of integrated energy systems in modern energy grids
• Chapter 2 Physical-economic models for integrated energy systems management
• Chapter 3 Decision-making tools for optimal operation and planning of integrated energy systems
• Chapter 4 Energy storage in integrated energy systems: an efficient way to overcome the flexibility deficiency
• Chapter 5 Towards efficient energy management using intelligent prediction models: a comparative study on energy consumption data
• Chapter 6 Application of machine learning techniques in managing integrated energy systems
• Chapter 7 Physics-aware machine learning for energy management of integrated energy systems
• Chapter 8 Machine learning-based methods for carbon emissions management in integrated energy systems
• Chapter 9 Physics-aware machine learning for sustainable development of integrated energy systems
• Chapter 10 Physics-informed neural networks for optimal integration of energy sources in future sustainable power systems
• Chapter 11 Data-driven energy scheduling of interconnected local energy networks flexibility: introducing an XGBoost-assisted cyber-security method
• Chapter 12 Physics-aware reinforcement learning for integrated energy systems management
• Chapter 13 Model-free reinforcement learning for integrated energy control of hybrid road vehicles
• Chapter 14 Physics-aware feature learning for integrated energy systems management
• Chapter 15 Physics-aware neural networks for integrated energy systems management
• Index