Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies

1st Edition - March 18, 2022
Editors: Krishna Kumar, Ram Shringar Rao, Omprakash Kaiwartya, Shamim Kaiser, Sanjeevikumar Padmanaban
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 2 2 8 - 0
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 4 2 8 - 4

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stab… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
About the editors
Preface
Chapter 1: Application of alternative clean energy
Abstract
1.1: Introduction
1.2: Solar energy
1.3: Geothermal energy
1.4: Wind energy
1.5: Biomass energy
1.6: Ocean and tidal energy
1.7: Small, micro, and mini hydro plants
1.8: Case study
1.9: Conclusion
References
Chapter 2: Optimization of hybrid energy generation
Abstract
Acknowledgment
2.1: Introduction
2.2: RES data and uncertainty statistical analysis
2.3: Test case modifications and solution methodology
2.4: Results
2.5: Discussion and conclusion, future scope
References
Chapter 3: IoET-SG: Integrating internet of energy things with smart grid
Abstract
3.1: Introduction
3.2: Traditional grid
3.3: Smart grid
3.4: Internet of energy things (IoET)
3.5: IoET-SG system
3.6: Research challenges and future guidelines
3.7: Conclusion
References
Chapter 4: Evolution of high efficiency passivated emitter and rear contact (PERC) solar cells
Abstract
4.1: Introduction
4.2: Photon absorption and optical generation
4.3: Loss mechanisms in PERC solar cells
4.4: Carrier transport equations
4.5: PERC technology
4.6: Fabrication of PERC solar cells
4.7: Characterization equipment
4.8: Conclusion
References
Chapter 5: Online-based approach for frequency control of microgrid using biologically inspired intelligent controller
Abstract
5.1: Introduction
5.2: Test system description
5.3: Fuzzy logic controller
5.4: Particle swarm optimization (PSO)
5.5: Gray wolf optimization (GWO)
5.6: Results analysis
5.7: Conclusion
References
Chapter 6: Optimal allocation of renewable energy sources in electrical distribution systems based on technical and economic indices
Abstract
6.1: Introduction
6.2: Problem formulation
6.3: Cosine adapted whale optimization algorithm (CAWOA)
6.4: Results and discussion
6.5: Conclusions
References
Chapter 7: Optimization of renewable energy sources using emerging computational techniques
Abstract
7.1: Introduction
7.2: Sources of renewable energy
7.3: Artificial intelligence (AI)
7.4: Conclusion
References
Chapter 8: Advanced renewable dispatch with machine learning-based hybrid demand-side controller: The state of the art and a novel approach
Abstract
Acknowledgment
8.1: Introduction
8.2: Building energy demand forecasting with machine learning
8.3: Flexible demand-side management strategies
8.4: Machine learning-based advanced controllers
References
Chapter 9: A machine learning-based design approach on PCMs-PV systems with multilevel scenario uncertainty
Abstract
Acknowledgment
9.1: Introduction
9.2: Overview on PCMs-PV systems and operations
9.3: Mechanism for machine learning on performance prediction of nonlinear systems
9.4: Application of machine learning in PCMs-PV systems
9.5: Challenges and outlooks
References
Chapter 10: Agent-based peer-to-peer energy trading between prosumers and consumers with cost-benefit business models
Abstract
Acknowledgment
10.1: Introduction
10.2: Agent-based peer-to-peer energy trading with dynamic internal pricing
10.3: Blockchain and machine learning technologies in P2P energy trading
10.4: Electricity market and techno-economic incentives for P2P energy market
10.5: Challenges and outlook
References
Chapter 11: Machine learning-based hybrid demand-side controller for renewable energy management
Abstract
11.1: Introduction
11.2: Machine learning at a glance
11.3: Conclusion
References
Chapter 12: Prediction of energy generation target of hydropower plants using artificial neural networks
Abstract
12.1: Introduction
12.2: Artificial neural network (ANN)
12.3: Performance measurement parameters
12.4: Modeling and analysis
12.5: Conclusion
References
Chapter 13: Response surface methodology-based optimization of parameters for biodiesel production
Abstract
13.1: Introduction
13.2: Problem formulation
13.3: Mathematical model of biodiesel production
13.4: Methodology
13.5: Reaction conditions by RSM
13.6: Surface plot by different combinations in RSM model
13.7: Conclusion
References
Chapter 14: Reservoir simulation model for the design of irrigation projects
Abstract
14.1: Introduction
14.2: System description
14.3: Cost-benefit functions
14.4: Methodology
14.5: Simulation computations
14.6: Results and discussion
14.7: Response of Harabhangi irrigation project
14.8: Conclusion
References
Chapter 15: Effect of hydrofoils on the starting torque characteristics of the Darrieus hydrokinetic turbine
Abstract
15.1: Introduction
15.2: Investigated parameters for the Darrieus hydrokinetic turbine
15.3: Numerical simulation analysis
15.4: Results and discussion
15.5: Conclusions
References
Index

Krishna Kumar

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.

Affiliations and expertise

Research and Development Engineer, UJVN Ltd. (A Govt. of Uttarakhand Enterprises), India

Ram Shringar Rao

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.

Affiliations and expertise

Associate Professor, Department of Computer Science and Engineering of Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India

Omprakash Kaiwartya

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).

Affiliations and expertise

Senior Lecturer and Course Leader, MSc Engineering, School of Science & Technology, Nottingham Trent University (NTU), UK

Shamim Kaiser

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.

Affiliations and expertise

Professor, Institute of Information Technology of Jahangirnagar University, Savar, Dhaka, Bangladesh

Sanjeevikumar Padmanaban

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.

Affiliations and expertise

Professor, Department of Electrical Engineering, Information Technology, and Cybernetics, University of South-Eastern Norway, Norway

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Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies

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Krishna Kumar

Ram Shringar Rao

Omprakash Kaiwartya

Shamim Kaiser

Sanjeevikumar Padmanaban