
Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies
- 1st Edition - March 18, 2022
- Imprint: Elsevier
- 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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Request a sales quoteSustainable 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.
- Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment
- Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum
- Addresses the advanced field of renewable generation, from research, impact and idea development of new applications
- 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
- Edition: 1
- Published: March 18, 2022
- Imprint: Elsevier
- No. of pages: 416
- Language: English
- Paperback ISBN: 9780323912280
- eBook ISBN: 9780323914284
KK
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.
RR
Ram Shringar Rao
OK
Omprakash Kaiwartya
SK
Shamim Kaiser
SP