Artificial Intelligence in Clean Energy Systems
From Energy Hubs to Smart Grids
- 1st Edition - January 1, 2027
- Latest edition
- Editors: Ali Ahmadian, Ali Almansoori, Ali Elkamel
- Language: English
Artificial Intelligence in Clean Energy Systems: From Energy Hubs to Smart Grids investigates the integration of AI and machine learning into future clean and modern energy system… Read more
Description
Description
Artificial Intelligence in Clean Energy Systems: From Energy Hubs to Smart Grids investigates the integration of AI and machine learning into future clean and modern energy systems, focusing on applying such technologies to optimal operation of energy hubs, smart grids, and efficient utilization of renewable energy sources. The book provides an in-depth overview of AI-based forecasting techniques, optimization, automation, and control approaches that enhance energy efficiency and sustainability in modern energy systems. It presents significant issues such as AI-based load demand forecasting, smart grid automation, integration of renewable energy, cybersecurity, and electric vehicle charging demand scheduling
Key features
Key features
- Covers AI-driven forecasting, optimization and automation techniques specifically tailored for energy hubs and smart grid
- Includes industry case studies and real-world implementations of AI in clean energy systems, helping readers bridge theory with practice
- Explores the latest advancements in AI, machine learning, and deep learning for energy management, making it a valuable resource for researchers and professionals
- Provides AI-based solutions to key energy challenges such as demand forecasting, grid stability, renewable energy integration, and cybersecurity
Readership
Readership
Researchers and academics: scholars in electrical engineering, mechanical engineering, chemical engineering, energy systems, and machine learning
Table of contents
Table of contents
Part I: Foundations of AI in Clean Energy
1. Introduction to AI in Clean Energy Systems
Overview of AI applications in energy
Role of energy hubs and smart grids
Importance of sustainability and efficiency
2. Fundamentals of Energy Hubs and Smart Grids
Energy hubs: concept, components, and benefits
Smart grids: architecture, challenges, and AI integration
Decentralized vs. centralized energy management
3. Artificial Intelligence and Machine Learning in Energy Systems
Basics of AI, ML, and deep learning
Key AI techniques in energy (optimization, forecasting, control)
Case studies of AI in energy applications
Part II: AI for Optimization and Forecasting in Energy Hubs
4. AI-Driven Energy Demand Forecasting
Time series forecasting techniques
AI models for load prediction (ANNs, LSTMs, etc.)
Real-world applications and challenges
5. Optimization of Energy Hubs Using AI
AI for energy scheduling and resource allocation
Reinforcement learning for dynamic energy management
Hybrid AI approaches for multi-objective optimization
6. Renewable Energy Integration with AI
AI-based solar and wind power forecasting
Grid stability and energy storage optimization
Case studies on AI-driven renewable energy solutions
Part III: AI Applications in Smart Grids
7. Smart Grid Automation and Control with AI
AI-powered grid monitoring and fault detection
Demand response and real-time energy management
Role of IoT and edge computing in AI-enabled grids
8. Cybersecurity and AI in Smart Energy Systems
AI for anomaly detection and threat mitigation
Blockchain and AI synergy for grid security
Privacy and ethical considerations in AI-driven energy systems
9. Electric Vehicles and AI-Driven Smart Charging
AI in vehicle-to-grid (V2G) integration
Smart charging strategies using machine learning
Impact of EVs on energy hubs and grid stability
Part IV: Future Trends and Case Studies
10. AI-Enabled Decentralized Energy Markets
Peer-to-peer energy trading with AI
AI in decentralized power generation and microgrids
Business models and economic impacts
11. Real-World AI Implementations in Clean Energy
Case studies of AI applications in energy hubs and grids
Lessons learned from global AI-driven energy projects
Challenges and opportunities for future development
12. The Future of AI in Clean Energy Systems
Emerging AI technologies for sustainable energy
Policy and regulatory considerations
Roadmap for AI-powered clean energy innovation
1. Introduction to AI in Clean Energy Systems
Overview of AI applications in energy
Role of energy hubs and smart grids
Importance of sustainability and efficiency
2. Fundamentals of Energy Hubs and Smart Grids
Energy hubs: concept, components, and benefits
Smart grids: architecture, challenges, and AI integration
Decentralized vs. centralized energy management
3. Artificial Intelligence and Machine Learning in Energy Systems
Basics of AI, ML, and deep learning
Key AI techniques in energy (optimization, forecasting, control)
Case studies of AI in energy applications
Part II: AI for Optimization and Forecasting in Energy Hubs
4. AI-Driven Energy Demand Forecasting
Time series forecasting techniques
AI models for load prediction (ANNs, LSTMs, etc.)
Real-world applications and challenges
5. Optimization of Energy Hubs Using AI
AI for energy scheduling and resource allocation
Reinforcement learning for dynamic energy management
Hybrid AI approaches for multi-objective optimization
6. Renewable Energy Integration with AI
AI-based solar and wind power forecasting
Grid stability and energy storage optimization
Case studies on AI-driven renewable energy solutions
Part III: AI Applications in Smart Grids
7. Smart Grid Automation and Control with AI
AI-powered grid monitoring and fault detection
Demand response and real-time energy management
Role of IoT and edge computing in AI-enabled grids
8. Cybersecurity and AI in Smart Energy Systems
AI for anomaly detection and threat mitigation
Blockchain and AI synergy for grid security
Privacy and ethical considerations in AI-driven energy systems
9. Electric Vehicles and AI-Driven Smart Charging
AI in vehicle-to-grid (V2G) integration
Smart charging strategies using machine learning
Impact of EVs on energy hubs and grid stability
Part IV: Future Trends and Case Studies
10. AI-Enabled Decentralized Energy Markets
Peer-to-peer energy trading with AI
AI in decentralized power generation and microgrids
Business models and economic impacts
11. Real-World AI Implementations in Clean Energy
Case studies of AI applications in energy hubs and grids
Lessons learned from global AI-driven energy projects
Challenges and opportunities for future development
12. The Future of AI in Clean Energy Systems
Emerging AI technologies for sustainable energy
Policy and regulatory considerations
Roadmap for AI-powered clean energy innovation
Product details
Product details
- Edition: 1
- Latest edition
- Published: January 1, 2027
- Language: English
About the editors
About the editors
AA
Ali Ahmadian
Dr Ali Ahmadian is a research associate with the Department of Chemical Engineering at the University of Waterloo, Canada. He holds a PhD in Electrical Engineering with a strong background in power and energy systems analysis. His research interests include transportation electrification, energy and environment, energy economics, and smart grid
Affiliations and expertise
Rresearch Associate, Department of Chemical Engineering, CanadaAA
Ali Almansoori
Ali Almansoori, Ph.D., is Professor of Chemical Engineering and Associate Provost for Education at Khalifa University - Abu Dhabi. He holds a BSc in Chemical Engineering with highest distinction from the Florida Institute of Technology, PhD in Chemical Engineering in the area of Process Systems Engineering from Imperial College London, and Executive MBA from London Business School. His specific research interests are in computer-aided modelling, optimization and simulation with applications to energy system design, sustainable operations and supply chain management
Affiliations and expertise
Professor of Chemical Engineering and Associate Provost for Education, Khalifa University - Abu Dhabi, United Arab EmiratesAE
Ali Elkamel
Ali Elkamel, Ph.D., is a Professor of Chemical Engineering at Khalifa University, UAE and the University of Waterloo, Canada. Professor Elkamel holds a BSc in Chemical Engineering and BSc in Mathematics from Colorado School of Mines, an MS in Chemical Engineering from the University of Colorado Boulder, and a Ph.D. in Chemical Engineering from Purdue University. His specific research interests are in computer-aided modeling, optimization, and simulation with applications to energy production planning, carbon management, sustainable operations, and product design. He is currently focusing on research projects related to gas production and processing, integration of renewable energy in oil and gas operations, and the utilization of data analytics (digitalization), machine learning, and artificial intelligence (AI) to improve the process and enterprise-wide efficiency and profitability
Affiliations and expertise
Professor of Chemical Engineering, Khalifa University, UAE and the University of Waterloo, Canada