Artificial Intelligence for Renewable Energy and Climate Change

Herausgegeben:Vasant, Pandian; Weber, Gerhard-Wilhelm; Thomas, J. Joshua; Marmolejo-Saucedo, José Antonio; Rodriguez-Aguilar, Roman

• Gebundenes Buch

Produktbeschreibung

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY AND CLIMATE CHANGE

Written and edited by a global team of experts in the field, this groundbreaking new volume presents the concepts and fundamentals of using artificial intelligence in renewable energy and climate change, while also covering the practical applications that can be utilized across multiple disciplines and industries, for the engineer, the student, and other professionals and scientists.

Renewable energy and climate change are two of the most important and difficult issues facing the world today. The state of the art in these areas is changing rapidly, with new techniques and theories coming online seemingly every day. It is important for scientists, engineers, and other professionals working in these areas to stay abreast of developments, advances, and practical applications, and this volume is an outstanding reference and tool for this purpose.

The paradigm in renewable energy and climate changeshifts constantly. In today's international and competitive environment, lean and green practices are important determinants to increase performance. Corresponding production philosophies and techniques help companies diminish lead times and costs of manufacturing, improve delivery on time and quality, and at the same time become more ecological by reducing material use and waste, and by recycling and reusing. Those lean and green activities enhance productivity, lower carbon footprint and improve consumer satisfaction, which in reverse makes firms competitive and sustainable.

This practical, new groundbreaking volume:
_ Features coverage on a wide range of topics such as classical and nature-inspired optimization and optimal control, hybrid and stochastic systems
_ Is ideally designed for engineers, scientists, industrialist, academicians, researchers, computer and information technologists, sustainable developers, managers, environmentalists, government leaders, research officers, policy makers, business leaders and students
_ Is useful as a practical tool for practitioners in the fields of sustainable and renewable energy sustainability
_ Includes wide coverage of how artificial intelligence can be used to impact the struggle against global warming and climate change

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Produktdetails

• Verlag: Wiley & Sons / Wiley-Scrivener
• Artikelnr. des Verlages: 1W119768990
• 1. Auflage
• Seitenzahl: 496
• Erscheinungstermin: 23. August 2022
• Englisch
• Abmessung: 236mm x 157mm x 37mm
• Gewicht: 453g
• ISBN-13: 9781119768999
• ISBN-10: 1119768993
• Artikelnr.: 63980361

Autorenporträt

Pandian Vasant, PhD, is Editor-in-Chief of the International Journal of Energy Optimization and Engineering and senior research associate at MERLIN Research Centre of Ton Duc Thang University, HCMC, Vietnam. He has 31 years of teaching experience and has co-authored over 300 publications, including research articles in journals, conference proceedings, presentations and book chapters. He has also been a guest editor for various scientific and technical journals. Gerhard-Wilhelm Weber, PhD, is a professor at Poznan University of Technology, Poznan, Poland. He received his PhD in mathematics, and economics / business administration, from RWTH Aachen. He held professorships by proxy at University of Cologne, and TU Chemnitz, Germany. J. Joshua Thomas, PhD, has been a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD in intelligent systems techniques in 2015 from University Sains Malaysia, Penang, and is an editorial board member for the International Journal of Energy Optimization and Engineering. He has also published more than 30 papers in leading international conference proceedings and peer reviewed journals. Jose A. Marmolejo Saucedo, PhD, is a professor at Pan-American University, Mexico. He received his PhD in operations research at the National Autonomous University of Mexico and has co-authored numerous research articles in scientific and scholarly journals, conference proceedings, presentations, books, and book chapters. Roman Rodriguez-Aguilar, PhD, is a professor in the School of Economic and Business Sciences of the "Universidad Panamericana" in Mexico. He received his PhD at the School of Economics at the National Polytechnic Institute, Mexico and has co-authored multiple research articles in scientific and scholarly journals, conference proceedings, presentations, and book chapters.

Inhaltsangabe

Preface xv

Section I: Renewable Energy 1

1 Artificial Intelligence for Sustainability: Opportunities and Challenges 3
Amany Alshawi

1.1 Introduction 3

1.2 History of AI for Sustainability and Smart Energy Practices 4

1.3 Energy and Resources Scenarios on the Global Scale 5

1.4 Statistical Basis of AI in Sustainability Practices 6

1.4.1 General Statistics 6

1.4.2 Environmental Stress-Based Statistics 8

1.4.2.1 Climate Change 9

1.4.2.2 Biodiversity 10

1.4.2.3 Deforestation 10

1.4.2.4 Changes in Chemistry of Oceans 10

1.4.2.5 Nitrogen Cycle 10

1.4.2.6 Water Crisis 11

1.4.2.7 Air Pollution 11

1.5 Major Challenges Faced by AI in Sustainability 11

1.5.1 Concentration of Wealth 11

1.5.2 Talent-Related and Business-Related Challenges of AI 12

1.5.3 Dependence on Machine Learning 14

1.5.4 Cybersecurity Risks 15

1.5.5 Carbon Footprint of AI 16

1.5.6 Issues in Performance Measurement 16

1.6 Major Opportunities of AI in Sustainability 17

1.6.1 AI and Water-Related Hazards Management 17

1.6.2 AI and Smart Cities 18

1.6.3 AI and Climate Change 21

1.6.4 AI and Environmental Sustainability 23

1.6.5 Impacts of AI in Transportation 24

1.6.6 Opportunities in Disaster Forecasting and Deforestation Forecasting 25

1.6.7 Opportunities in the Energy Sector 26

1.7 Conclusion and Future Direction 26

References 27

2 Recent Applications of Machine Learning in Solar Energy Prediction 33
N. Kapilan, R.P. Reddy and Vidhya P.

2.1 Introduction 34

2.2 Solar Energy 34

2.3 AI, ML and DL 36

2.4 Data Preprocessing Techniques 38

2.5 Solar Radiation Estimation 38

2.6 Solar Power Prediction 43

2.7 Challenges and Opportunities 45

2.8 Future Research Directions 46

2.9 Conclusion 46

Acknowledgement 47

References 47

3 Mathematical Analysis on Power Generation - Part I 53
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy

3.1 Introduction 54

3.2 Methodology for Derivations 55

3.3 Energy Discussions 59

3.4 Data Analysis 63

Acknowledgement 67

References 67

Supplementary 69

4 Mathematical Analysis on Power Generation - Part II 87
G. Udhaya Sankar, C. Ganesa Moorthy and C.T. Ramasamy

4.1 Energy Analysis 88

4.2 Power Efficiency Method 89

4.3 Data Analysis 91

Acknowledgement 96

References 97

Supplementary - II 100

5 Sustainable Energy Materials 117
G. Udhaya Sankar

5.1 Introduction 117

5.2 Different Methods 119

5.2.1 Co-Precipitation Method 119

5.2.2 Microwave-Assisted Solvothermal Method 120

5.2.3 Sol-Gel Method 120

5.3 X-R ay Diffraction Analysis 120

5.4 FTIR Analysis 122

5.5 Raman Analysis 124

5.6 UV Analysis 125

5.7 SEM Analysis 127

5.8 Energy Dispersive X-Ray Analysis 127

5.9 Thermoelectric Application 129

5.9.1 Thermal Conductivity 129

5.9.2 Electrical Conductivity 131

5.9.3 Seebeck Coefficient 131

5.9.4 Power Factor 132

5.9.5 Figure of Merit 133

5.10 Limitations and Future Direction 133

5.11 Conclusion 133

Acknowledgement 134

References 134

6 Soft Computing Techniques for Maximum Power Point Tracking in Wind