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Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies - (Paperback)
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Highlights
- 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.
- Author(s): Krishna Kumar & Ram Shringar Rao & Omprakash Kaiwartya & Shamim Kaiser & Sanjeevikumar Padmanaban
- 416 Pages
- Science, Energy
Description
About the Book
"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"--Book Synopsis
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.