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Applications of Artificial Intelligence in New Energy Technology Systems

Edited by
September 2023
198 pages
  • ISBN978-3-0365-8416-4 (Hardback)
  • ISBN978-3-0365-8417-1 (PDF)
https://doi.org/10.3390/books978-3-0365-8417-1
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This book is a reprint of the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems that was published in

Business & Economics
Environmental & Earth Sciences
Social Sciences, Arts & Humanities
Summary

“Advancements in Energy Technologies: Optimizing Efficiency, Control, and Sustainability" is a comprehensive collection of selected papers that highlight the latest research and innovations in the field of energy technologies. This reprint showcases cutting-edge advancements in areas such as artificial intelligence, optimization techniques, control systems, and renewable energy. With a focus on improving efficiency, enhancing control strategies, and promoting sustainable practices, it provides valuable insights for researchers, academics, and professionals. Covering a wide range of topics, this collection presents a glimpse into the future of energy systems and offers solutions to address the challenges of a rapidly changing energy landscape.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
power plant; electrical power modeling; metaheuristic optimization; water cycle algorithm; machine learning; deep learning; big data; energy; deep learning; smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks; energy-efficient building; heating load; neural computing; biogeography-based optimization; big data; machine learning; artificial intelligence; deep learning; building energy; smart buildings, IoT; smart city; self-evolving; nonlinear consequent part; convergence analysis; renewable energy; type-2 fuzzy; artificial intelligence; machine learning; big data; data science; fuzzy logic; energy; nano-refrigerant; nanofluid; refrigerator; energy efficiency; thermodynamic analysis; aluminum oxide; fuel economy; fuel consumption; energy savings; emissions mitigation; CO2 emissions; Malaysia; crossover switches cell; CSC; multilevel inverter; Packed-U-Cell; model predictive control; grid connection; diesel; oxyhydrogen; artificial neural network; response surface methodology; prediction; desirability; microgrid; energy management system; restoration; power quality; policy market