Green Energy Integration with Emerging Energy Infrastructure and Artificial Intelligence

Erscheinungsjahr 2027
ISBN: 978-3-032-25920-2
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 428 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Green Energy and Technology

The global transition to sustainable energy systems is accelerating, fueled by rapid advancements in energy infrastructure and artificial intelligence (AI). This book presents selected papers from the 10th International Conference on Renewable Energy and Conservation (ICREC 2025). It explores cutting-edge research and emerging solutions for integrating green energy sources with modern energy systems. The focus is on the intersection of renewable energy systems (RESSs) with AI-driven optimization, grid technologies, and next-generation infrastructure. Topics covered include the transformation of energy from RESSs to smart grids, hybrid energy systems, and the application of AI and machine learning to grid management. The book also addresses computational optimization methods and strategic energy policies. This volume presents a multidisciplinary perspective by bridging research, industry, and policy, offering insights into technological innovations, decision-support systems, and real-world applications.

is a crucial resource for academics, engineers, policymakers, and professionals seeking to understand the evolving landscape of green energy integration. It covers policy strategies, modernization of energy infrastructure, and AI-driven energy management.

Zielgruppe

Research

Autoren/Hrsg.

Pong, Philip

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Sozialwissenschaften Politikwissenschaft Regierungspolitik
• Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien
• Geowissenschaften Umweltwissenschaften Umweltwissenschaften

Inhaltsverzeichnis

Comparative Analysis of Deep Learning Models for Wind Speed Forecasting.- Improvement on Mechanical Performance of Darrieus-Savonius Hybrid Wind Turbine by Using Opening/closing Mechanism of Savonius Rotor.- Improving Energy Performance of a Solar Container House in Algeria: Insights from Thermal Modeling.- Multi-Objective Optimization of a Solar-Assisted Combined Cooling, Heating, and Power Generation System Using the Genetic Algorithm Optimizer.- A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization.- Enhancing the Reliability and Efficiency of 115/22 kV Electrical Substations in Thailand: Design Principles and Operational Strategies.- An Enhanced Optimisation Strategy in a Hybrid Power System with Photovoltaic-Wind Turbine System in Terms of Energy Conservation.- Assessment of Impact of Renewable Energy Penetration on Network Stability.- Energy Optimization of Building Design Using Genetic Algorithm and RMP Ranking Method.

Über Autor(innen)

Philip Pong, Ph.D. , is an Associate Professor of Electrical and Computer Engineering at the New Jersey Institute of Technology, where he is the Director of the Power Systems Engineering Center and the Sensors Laboratory. He received a Ph.D. in electrical engineering from the University of Cambridge. Dr. Pong was a three-year postdoctoral researcher at the National Institute of Standards and Technology in the United States. His current research interests focus on developing and applying advanced electromagnetic sensing techniques for smart grids, smart cities, and clean energy. Dr. Pong is a Fellow of the Institution of Engineering and Technology, the Institute of Physics, the Energy Institute, the Institute of Materials, Minerals, and Mining, and the Hong Kong Institution of Engineers. He is a Chartered Physicist, a Chartered Engineer, a Chartered Energy Engineer, a Registered Professional Engineer, and a Senior Member of the IEEE. He serves on the editorial boards for several IEEE and SCI journals.