Artificial Intelligence in Clean Energy Systems
From Energy Hubs to Smart Grids
Elsevier (Publisher)
Will be published approx. on 1. January 2027
Book
Paperback/Softback
300 pages
978-0-443-45199-7 (ISBN)
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
More details
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 152 mm
Weight
449 gr
ISBN-13
978-0-443-45199-7 (9780443451997)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
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 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 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
Editor
Rresearch Associate, Department of Chemical Engineering, Canada
Professor of Chemical Engineering and Associate Provost for Education, Khalifa University - Abu Dhabi, United Arab Emirates
Professor of Chemical Engineering, Khalifa University, UAE and the University of Waterloo, Canada
Content
Part I: Foundations of AI in Clean Energy
1. Introduction to AI in Clean Energy Systems
2. Fundamentals of Energy Hubs and Smart Grids
3. Artificial Intelligence and Machine Learning in Energy Systems
Part II: AI for Optimization and Forecasting in Energy Hubs
4. AI-Driven Energy Demand Forecasting
5. Optimization of Energy Hubs Using AI
6. Renewable Energy Integration with AI
Part III: AI Applications in Smart Grids
7. Smart Grid Automation and Control with AI
8. Cybersecurity and AI in Smart Energy Systems
9. Electric Vehicles and AI-Driven Smart Charging
Part IV: Future Trends and Case Studies
10. AI-Enabled Decentralized Energy Markets
11. Real-World AI Implementations in Clean Energy
12. The Future of AI in Clean Energy Systems
1. Introduction to AI in Clean Energy Systems
2. Fundamentals of Energy Hubs and Smart Grids
3. Artificial Intelligence and Machine Learning in Energy Systems
Part II: AI for Optimization and Forecasting in Energy Hubs
4. AI-Driven Energy Demand Forecasting
5. Optimization of Energy Hubs Using AI
6. Renewable Energy Integration with AI
Part III: AI Applications in Smart Grids
7. Smart Grid Automation and Control with AI
8. Cybersecurity and AI in Smart Energy Systems
9. Electric Vehicles and AI-Driven Smart Charging
Part IV: Future Trends and Case Studies
10. AI-Enabled Decentralized Energy Markets
11. Real-World AI Implementations in Clean Energy
12. The Future of AI in Clean Energy Systems