Renewable Energy Systems: Modelling, Optimization and Control - Brossura

Brossura

ISBN 10: 0128200049 ISBN 13: 9780128200049

Casa editrice: Academic Pr, 2021

Riassunto

Renewable Energy Systems: Modelling, Optimization and Control aims to cross-pollinate recent advances in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems by leading researchers. The book brings together the most comprehensive collection of modeling, control theorems and optimization techniques to help solve many scientific issues for researchers in renewable energy and control engineering. Many multidisciplinary applications are discussed, including new fundamentals, modeling, analysis, design, realization and experimental results. The book also covers new circuits and systems to help researchers solve many nonlinear problems.

This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work.

Su questo libro

Informazioni sull?autore

Prof. Ahmad Azar is a full Professor at Prince Sultan University, Riyadh, Kingdom Saudi Arabia. He is the leader of Automated Systems and Computing Lab (ASCL), Prince Sultan University, Saudi Arabia.

Prof. Azar is the Editor in Chief of the International Journal of Intelligent Engineering Informatics (IJIEI), Inderscience Publishers, Olney, UK. He is also the Editor in Chief of International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) and International Journal of Sociotechnology and Knowledge Development (IJSKD) published by IGI Global, USA. From 2013 to 2017, Prof. Azar was an associate editor of ISA Transactions, Elsevier.

He is currently an editor for IEEE Transactions on Fuzzy Systems, IEEE Systems Journal, IEEE Transactions on Neural Networks and Learning Systems, Springer's Human-centric Computing and Information Sciences.

Prof. Azar specializes in artificial intelligence (AI), robotics, machine learning, control theory and applications, computational intelligence, reinforcement learning, and dynamic system modeling. He has published or co-published over 550 research papers, book chapters, and conference proceedings in prestigious peer-reviewed journals.

Dr. Ahmad Azar has received several awards, including the Benha University Prize for Scientific Excellence (2015, 2016, 2017, and 2018) and the Benha University Highest Citation Award (2015, 2016, 2017, and 2018).

In June 2018, he was awarded the Egyptian State Encouragement Award in Engineering Sciences by the Ministry of Higher Education and Scientific Research. In August 2018, he was elected as a senior member of the International Rough Set Society (IRSS).

Prof. Azar was named one of the top computer scientists in Saudi Arabia by Research.com since December 2019.

He was awarded the Egyptian President's Distinguished Egyptian Order of the First Class in February 2020.

In October 2020, Prof. Azar received Abdul Hameed Shoman Arab Researchers Award in Machine Learning and Big Data Analytics.

From October 2020 to September 2023, Prof. Azar was recognized as a Distinguished researcher at Prince Sultan University, Riyadh, Saudi Arabia.

In November 2020, October 2021, October 2022, October 2023, September 2024, and September 2025 Prof. Azar was named one of the top 2% of scientists in the world in Artificial Intelligence by Stanford University, based on single-year impact and career-long impact. These rankings were published by Stanford University in the PLOS journal and were based on the SCOPUS database.

Prof. Ahmad Azar has been recognized as one of the top ten researchers at Prince Sultan University, based on his Scopus H-index. He has also received a university award for being among his top publication of research.

Prof. Azar has received Prince Sultan University’s Research Excellence Award. He is also the recipient of the university’s Highest Impact Researcher Award, based on his H-index. Additionally, he has earned a PSU research award for having publications ranked among the top five by impact factor.

Prof. Ahmad Azar is the Vice Chair of the International Federation of Automatic Control (IFAC) Technical Committee of Control Design, Vice chair of IFAC Technical committee 4.3 Robotics, Vice chair of IFAC Technical committee 9.3 “Control for Smart Cities”. He is a technical Committee Member of Data Mining and Big Data Analytics of IEEE Computational Intelligence Society (CIS), IFAC Technical committee Member TC 2.2. Linear Control Systems, IFAC Technical committee Member TC 1.2. Adaptive and Learning Systems.

Dalla quarta di copertina

Renewable Energy Systems: Modelling, Optimization and Control aims to cross-pollinate recent advances in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems by leading energy systems engineering researchers in the field.

This book brings together the most comprehensive collection of modeling, control theorems, optimization techniques, which will solve many scientific issues for researchers in the fields of renewable energy and control engineering, and can be considered a key reference for young researchers. Many multi-disciplinary applications are discussed with new fundamentals, modeling, analysis, design, realization, and experimental results. The book also covers new circuits and systems which will offer practical solutions for researchers to solve many nonlinear problems. This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work.

Dettagli dell'edizione

• Editore: Academic Pr
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 0128200049
• ISBN 13: 9780128200049
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 600
• Redattore: Azar Ahmad Taher, Kamal Nashwa Ahmad
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile