Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern.
Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy.
Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry.
1. Advances of Artificial Intelligence in a Green Energy Environment - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. About the editors - p. Page vi 8. Preface - p. Page viii 9. Acknowledgments - p. Page xiv 10. List of Illustrations - p. Page xv 11. List of Tables - p. Page xvi 12. Chapter 1 : Application of some ways to intensify the process of anaerobic bioconversion of organic matter - p. Page xvi 13. Chapter 2 : Disasters impact assessment based on socioeconomic approach - p. Page 28 14. Chapter 3 : Uninterruptible power supply system of the consumer, reducing peak network loads - p. Page 52 15. Chapter 4 : Optimization of the anaerobic conversion of green biomass into volatile fatty acids for further production of high-calorie liquid fuel - p. Page 64 16. Chapter 5 : Life cycle cost and life cycle assessment: an approximation to understand the real impacts of the Electricity Supply Industry - p. Page 80 17. Chapter 6 : Comparison of open access multi-objective optimization software tools for standalone hybrid renewable energy systems - p. Page 108 18. Chapter 7 : Optimization of the organic waste anaerobic digestion in biogas plants through the use of a vortex layer apparatus - p. Page 126 19. Chapter 8 : Search of regularities in data: optimality, validity, and interpretability - p. Page 149 20. Chapter 9 : Artificial intelligence techniques for modeling of wind energy harvesting systems: a comparative analysis - p. Page 170 21. Chapter 10 : Human paradigm and reliability for aggregate production planning under uncertainty - p. Page 188 22. Chapter 11 : Artificial intelligence–based intelligent geospatial analysis in disaster management - p. Page 200 23. Chapter 12 : Optimizing the daily use of limited solar panels in closely located rural schools in Zimbabwe - p. Page 219 24. Chapter 13 : Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics - p. Page 242 25. Chapter 14 : Hybrid optimization and artificial intelligence applied to energy systems: a review - p. Page 268 26. Chapter 15 : A brief literature review of quantitative models for sustainable supply chain management - p. Page 294 27. Chapter 16 : Optimized designing spherical void structures in 3D domains - p. Page 322 28. Chapter 17 : Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles - p. Page 344 29. Index - p. Page 371
Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern.
Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy.
Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry.
1. Advances of Artificial Intelligence in a Green Energy Environment - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. About the editors - p. Page vi 8. Preface - p. Page viii 9. Acknowledgments - p. Page xiv 10. List of Illustrations - p. Page xv 11. List of Tables - p. Page xvi 12. Chapter 1 : Application of some ways to intensify the process of anaerobic bioconversion of organic matter - p. Page xvi 13. Chapter 2 : Disasters impact assessment based on socioeconomic approach - p. Page 28 14. Chapter 3 : Uninterruptible power supply system of the consumer, reducing peak network loads - p. Page 52 15. Chapter 4 : Optimization of the anaerobic conversion of green biomass into volatile fatty acids for further production of high-calorie liquid fuel - p. Page 64 16. Chapter 5 : Life cycle cost and life cycle assessment: an approximation to understand the real impacts of the Electricity Supply Industry - p. Page 80 17. Chapter 6 : Comparison of open access multi-objective optimization software tools for standalone hybrid renewable energy systems - p. Page 108 18. Chapter 7 : Optimization of the organic waste anaerobic digestion in biogas plants through the use of a vortex layer apparatus - p. Page 126 19. Chapter 8 : Search of regularities in data: optimality, validity, and interpretability - p. Page 149 20. Chapter 9 : Artificial intelligence techniques for modeling of wind energy harvesting systems: a comparative analysis - p. Page 170 21. Chapter 10 : Human paradigm and reliability for aggregate production planning under uncertainty - p. Page 188 22. Chapter 11 : Artificial intelligence–based intelligent geospatial analysis in disaster management - p. Page 200 23. Chapter 12 : Optimizing the daily use of limited solar panels in closely located rural schools in Zimbabwe - p. Page 219 24. Chapter 13 : Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics - p. Page 242 25. Chapter 14 : Hybrid optimization and artificial intelligence applied to energy systems: a review - p. Page 268 26. Chapter 15 : A brief literature review of quantitative models for sustainable supply chain management - p. Page 294 27. Chapter 16 : Optimized designing spherical void structures in 3D domains - p. Page 322 28. Chapter 17 : Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles - p. Page 344 29. Index - p. Page 371
