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Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies
A comprehensive book on methodologies of AI and machine learning for renewable system design, surrogate model development, characterization and optimization
Krishna Kumar (Edited by), Ram Shringar Rao (Edited by), Omprakash Kaiwartya (Edited by), Shamim Kaiser (Edited by), Sanjeevikumar Padmanaban (Edited by)
9780323912280, Elsevier Science
Paperback / softback, published 21 March 2022
416 pages, 75 illustrations (25 in full color)
22.9 x 15.2 x 2.6 cm, 0.66 kg
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.
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
Subject Areas: Alternative & renewable energy sources & technology [THX]
