About the Book
Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. 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 provide sustainable development.
As 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, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation.
Table of Contents:
1. Application of Alternative Clean Energy
2. Optimization of Hybrid energy generation
3. IoET-SG: Integrating Internet of Energy Things with Smart Grid
4. Evolution of High Efficiency PERC Solar Cells
5. Online Based Approach for Frequency Control of Micro- Grid Using Biological Inspired Based Intelligent Controller
6. Optimal Allocation of Renewable Energy Sources in Electrical Distribution Systems Based on Technical and Economic Indexes
7. Optimization of Renewable Energy Sources Using Emerging Computational Techniques
8. Advanced renewable dispatch with machine-learning based hybrid demand-side controller: state-of-the-art and a novel approach
9. Machine learning-based robust and reliable design on PCMs-PV systems with multi-level scenario uncertainty
10. Agent-based peer-to-peer energy trading between prosumers and consumers with cost-benefit business models
11. Machine learning-based hybrid demand-side controller for renewable energy management
12. Prediction of Energy Generation Target of Hydropower Plants using Artificial Neural Network
13. Response surface methodology based optimization of Parameters for Biodiesel Production
14. Reservoir Simulation Model for the Design of Irrigation Project
15. Effect of Hydrofoils on the Starting Torque Characteristics of Darrieus Hydrokinetic Turbine
About the Author :
Dr. Krishna Kumar received his BE degree in Electronics and Communication Engineering from Govind Ballabh Pant Engineering College, Pauri Garhwal, Uttarakhand, India, MTech degree in Digital Systems from Motilal Nehru NIT, Allahabad, India, in 2006 and 2012, respectively, and PhD degree in the Department of Hydro and Renewable Energy at the Indian Institute of Technology Roorkee, India, in 2023.
He is currently working as an Assistant Engineer at UJVN Ltd. (a State Government PSU of Uttarakhand) since January 2013. Before joining UJVNL, he worked as an Assistant Professor at BTKIT, Dwarahat (a Government of Uttarakhand Institution). He has published numerous research papers in international journals and conferences, including IEEE, Elsevier, Springer, MDPI, Hindawi, and Wiley. He has also edited and written books for Taylor & Francis, Elsevier, Springer, River Press, and Wiley. His current research interests include IoT, AI, and renewable energy.
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. 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). 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. Sanjeevikumar Padmanaban is a Full Professor in Electrical Power Engineering with the Department of Electrical Engineering, Information Technology, and Cybernetics of the University of South-Eastern Norway, Norway. He has over a decade of academic and teaching experience, including Associate/Assistant Professorships at the University of Johannesburg, South Africa (2016-2018), Aalborg University, Denmark (2018-2021) and the CTIF Global Capsule Laboratory at Aarhus University, Denmark (2021-present). Prof. Padmanaban received a lifetime achievement award from Marquis Who’s Who - USA 2017 for contributing to power electronics and renewable energy research, and was listed among the world’s top 2% of scientists by Stanford University, USA in 2019.
