Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
Developments in the capacity for building renewable energy generation systems has sparked a paradigm shift in the energy structure and the sustainability sector. Sustainable Developments with Artificial Intelligence and Machine Learning for Renewable Energies analyses the changes in this energy generation shift, including issues of grid stability with the variability in renewable energy verses traditional baseload energy generation. The use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, and machine learning and the capabilities to design, model, characterize and forecast performance predictions for the optimization of renewable energy systems. Providing solutions to current critical environmental, economic, and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and sustainable development.
Targeted at researchers, academicians, and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation.
- Covers the best-performing methods and approaches for designing renewable energy systems where AI integration in a real-time environment with simulation results and online map hyperlinking
- Gives advanced techniques for monitoring current technologies, and how to efficiently utilize the energy grid spectrum
- Addresses the advance field of renewable generation, from research, impact, and idea development of new applications in a single platform
Researchers and scholars who are pursuing research in the field of renewable energy and AI
- Introduction of Artificial Intelligent and Machine Learning
2. Energy systems and the present status of renewable energy systems
3. Role of Artificial Intelligent in renewable energy
4. Energy market, demand analysis, and forecasting
5. E-Mobility- a growing energy trade market case study
6. Renewable energy generation forecasting, and operation & maintenance optimization
7. Primary & secondary parameters forecasting, and operation & maintenance optimization of hydropower plants
8. Hydrogen energy generation optimization
9. Optimization of Hybrid energy generation
10. Introduction –Blockchain and Smart Grid
11. Transformation of Smart Grid to IoE
12. Building Blockchain-based IoE Infrastructure
13. Energy storage technologies and their parameter optimization
14. Machine learning-based hybrid demand-side controller for renewable energy management
15. Machine learning-based robust and reliable design on PCMs-PV systems with multilevel scenario uncertainty
- No. of pages:
- © Academic Press 2022
- 1st May 2022
- Academic Press
- Paperback ISBN:
Er. Krishna Kumar is presently working as a Research and Development Engineer at UJVN Ltd. (A Govt. of Uttarakhand Enterprises). He has more than 13 years of experience in operation & maintenance, and design of hydropower plants. Before joining UJVNL he has also worked as Assistant Professor at BTKIT, Dwarahat (A Govt. of Uttarakhand Institution). He has completed his B.E. (Electronics and Communication Engineering) from Govind Ballabh Pant Engineering College, Pauri Garhwal (A Govt. of Uttarakhand Institution), M.Tech (Digital Systems) from Motilal Nehru NIT Allahabad (A Govt. of India Institution), and presently pursuing Ph.D. from Indian Institute of Technology, Roorkee. He has published numerous research papers in international journals and conferences including IEEE, Elsevier, and Springer. He has also edited and written books on Taylor & Francis, and Wiley which are under publication. His present research area includes IoT, AI, and Renewable Energy.
Research and Development Engineer, UJVN Ltd. (A Govt. of Uttarakhand Enterprises), India
Dr. Ram Shringar Rao received his Ph.D. (Computer Science and Technology) from School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi. He has worked as an Associate Professor in the Department of Computer Science, Indira Gandhi National Tribal and is currently Associate Professor in the Department of Computer Science and Engineering of Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India. He has more than 18 years of teaching, administrative and research experience. Dr. Rao has worked administrative works in the capacities of HOO (Head of Office, AIACTR), Member Academic Council (IGNTU), Chief Warden, Coordinator University Cultural Cell, Coordinator University Computer Center, HoD of Computer Sc. and Engg., Proctor, Warden, Member of BOS and Nodal Officer of Technical Education Quality Improvement Programme (TEQIP) etc.
Associate Professor, Department of Computer Science and Engineering of Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India
Dr. Omprakash Kaiwartya is a Senior Lecturer and Course Leader for MSc Engineering at the School of Science & Technology, Nottingham Trent University (NTU). He was a Research Associate at the Department of Computer and Information Science at Northumbria University, UK, and involved in the gLINK, European Union project. Prior to this, he was a Post-Doctoral Fellow in the Faculty of Computing, University of Technology (UTM), Malaysia. He has authored/co-authored over 100 international Journal articles, Conference Proceedings, Book Chapters, and books. Dr. Omprakash’s research focuses on IoT centric smart environment for diverse domain areas including Transport, Healthcare, and Industrial Production. His recent scientific contributions are in Internet of Connected Vehicles (IoV), E-Mobility, Electronic Vehicles Charging Management (EV), Internet of Healthcare Things (IoHT), Smart use case implementation of Sensor Networks, and Next Generation Wireless Communication Technologies (6G and Beyond).
Senior Lecturer and Course Leader, MSc Engineering, School of Science & Technology, Nottingham Trent University (NTU), UK
Dr. M. Shamim Kaiser is currently working as a Professor at the Institute of Information Technology of Jahangirnagar University, Savar, Dhaka-1342, Bangladesh. He received his Bachelor's and Master's degrees in Applied Physics Electronics and Communication Engineering from the University of Dhaka, Bangladesh in 2002 and 2004 respectively, and the Ph. D. degree in Telecommunication Engineering from the Asian Institute of Technology (AIT) Pathumthani, Thailand, in 2010. His current research interests include Data Analytics, Machine Learning, Wireless Network & Signal processing, Cognitive Radio Network, Big data and Cyber Security, Renewable Energy. He has authored more than 100 papers in different peer-reviewed journals and conferences and his google citation is more than 1020.
Professor, Institute of Information Technology of Jahangirnagar University, Savar, Dhaka, Bangladesh
Sanjeevikumar Padmanaban received the bachelor's degree from the University of Madras, the master's degree from Pondicherry University and the Ph.D. degree University of Bologna. Currently he is working as a Faculty Member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has authored 300 plus scientific papers and has received the Best Paper cum Most Excellence. He is a fellow the Institution of Engineers, FIE, India, fellow the Institution of Telecommunication and Electronics Engineers, FIETE, India and fellow the Institution of Engineering and Technology, IET, UK. He serves as an Editor/Associate Editor/Editorial Board of refereed journal, in particular, the IEEE Systems Journal, the IEEE Access Journal, the IET Power Electronics, Journal of Power Electronics, Korea, and the subject editor of the subject Editor of IET Renewable Power Generation, the subject Editor of IET Generation, Transmission and Distribution, and the subject editor of FACTS journal, Canada.
CTiF Global Capsule (CGC), Department of Business Development and Technology, Aarhus University, Herning, Denmark
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.