Applications of Deep Machine Learning in Future Energy Systems

Häftad, Engelska, 2024

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Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation before laying out current AI approaches and limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply.

An essential tool for the management, control, and modelling of future energy systems, this book maps a practical path towards AI capable of supporting sustainable energy.

Clarifies the current state and future trends of energy system machine learning and the pitfalls facing our transitioning systems
Provides guidance on 3rd-generation AI tools for meeting the challenges of modeling and control in modern energy systems
Includes case studies and practical examples of potential applications to inspire and inform researchers and industry developers

Produktinformation

Utgivningsdatum2024-08-21
Mått152 x 229 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor334
FörlagElsevier Science
ISBN9780443214325

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Energiteknik inom Naturvetenskap och teknik

Dr. Mohammad-Hassan Khooban is an Assistant Professor in the Department of Engineering and the Director of the Power Circuits and Systems Research Group at Aarhus University in Denmark. He has authored or co-authored more than 220 publications in peer-reviewed journals (mostly IEEE) and international conferences, written three book chapters, and holds one patent. He has been involved in six national and international projects. He was identified in 2019, 2020, and 2021 by Stanford University as one of the world’s top 2% researchers in engineering. He was also ranked 16th in the list of top 30 Electronics and Electrical Engineering Scientists in Denmark in 2022. His research interests include the application of advanced control, and optimization of artificial intelligence-inspired techniques in power electronics and systems.

1. Introduction2. Artificial intelligence and Machine learning in Future Energy Systems (State-of-Art, future development)Jalal Heidary3. Digital Twins-Assisted Design of Next-Generation DC MicrogridMeysam Gheisarnejad, Maryam Homayounzadeh, Burak Yildirim4. Deep Learning-Based Procedure for Profit Maximization of EV Charging StationsMohammad Hassan Khooban, Peyman Razmi, MASOUMEH SEYEDYAZDI5. Deep Frequency Control of Power Grids Under Cyber AttacksMohammad Aghamohammadi, jalal heidary, Soroush Oshnoei6. Application of Q-Learning in Stabilization of Multi Carrier Energy SystemsMeysam Gheisarnejad, Maryam Homayounzadeh, Burak Yildirim7. Design of Next-Generation of 5G Data Center Power Supply based on AIMohammad Hassan Khooban, Meysam Gheisarnejad8. Smart EV Battery Charger Based on Deep Machine LearningMohammad Hassan Khooban, Jalil Boudjadar, Mehdi Rafiei9. Machine learning in Talkative PowerMohammad Hassan Khooban, Zahra Ghahraman Izadi, Ali Mousavi10. Advanced Control of Power Electronics-based Machine LearningMaryam Homayounzadeh, Meysam Gheisarnejad, Mohamadreza Homayounzade, Mohammad Hassan Khooban11. Multi-Level Energy Management and Optimal Control System in Smart Cities Based on Deep Machine LearningJavid Ghafourian, Atefe Hedayatnia, Ahmed Al-Durra, Reza Sepehrzad

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