Preface xiii Inam UL HAQ, Sanna Mehraj KAK, Anand Kumar GUPTA and Muhammad Sher RAMZAN Chapter 1. AI for Solar Energy Forecasting and Optimization 1 Sarika AGARWAL, Mamta NARWARIA, Rohit KUMAR and Savita SINGH 1.1. Introduction 1 1.2. Fundamentals of solar energy and forecasting 3 1.3. AI techniques for forecasting 7 1.
4. Predicting solar energy output using a LSTM DL model 10 1.5. Applications and case studies 14 1.6. Challenges, risks and ethical considerations 16 1.7. Future directions and research opportunities 20 1.
8. Conclusion 21 1.9 References 22 Chapter 2. Integrating ANN, SVM and GA for Optimized Wind Resource Assessment and Energy Management 25 Vikrant SHARMA and Salliah SHAFI 2.1. Introduction 25 2.2. Literature review 29 2.
3. Methodology 33 2.4. Result and discussion 37 2.5. Conclusion and future work 39 2.6. References 39 Chapter 3.
Next-Generation Renewables: AI Applications in Green Hydrogen Production and Marine Energy Harvesting 43 Pardeep KUMAR, Sanjeev KUMAR and Mohammad Badruddoza TALUKDER 3.1. Introduction 43 3.2. Next-generation renewable energy systems: introduction 48 3.3. AI for renewable energy systems 51 3.4.
Application of AI in hydrogen green production 56 3.5. AI in marine energy harvesting 59 3.6. AI-intensified systems interaction with smart grids 62 3.7. Challenges and limitations 67 3.8.
Future research directions 71 3.9. Conclusions and recommendations 75 3.10. References 77Contents vii Chapter 4. Mapping the Research Landscape of AI in Electric Vehicles: A Bibliometric Analysis of Foundations and Emerging Trends 83 Nivedita JHA, Rakhi ARORA and Sonal PUROHIT 4.1. Introduction 83 4.
2. Methodology 85 4.3. Analysis and discussion 87 4.4. Most relevant authors 88 4.5. Most relevant affiliations.
90 4.6. Most cited countries 92 4.7. Most relevant journals 93 4.8. Keyword analysis 95 4.9.
Co-occurrence network analysis 95 4.10. Factorial analysis 97 4.11. Thematic map 100 4.12. Chronological mapping of the most influential documents 101 4.13.
Conclusion 102 4.14. References 103 Chapter 5. AI-Enabled Predictive and Adaptive Solutions for Environmental and Energy Challenges 107 Inderdeep KAUR 5.1. Introduction 107 5.2. AI technologies for environmental intelligence 109 5.
3. Predictive modeling for environmental challenges 113 5.4. Adaptive AI systems for sustainable management 116 5.5. AI for renewable energy optimization 121 5.6. Case studies and practical applications 126 5.
7. Challenges and ethical considerations 129 5.8. Future directions and emerging trends 133 5.9. Conclusion 137 5.10. References 138 Chapter 6.
Harnessing AI to Address Environmental and Energy Challenges in a Climate-Driven World 143 Sahil SHARMA and Rishi KANT 6.1. Introduction 144 6.2. AI technologies and environmental data management 146 6.3. Applications of AI in climate change mitigation 148 6.4.
Challenges and ethical considerations 151 6.5. Future perspectives and integrated technologies 154 6.6. Conclusion 157 6.7. References 157 Chapter 7. Global Policies and AI-Enabled Energy Strategies 165 Adil Husain RATHER, Inam UL HAQ and Irfan RASOOL 7.
1. Introduction 165 7.2. International laws influencing AI use in energy systems 168 7.3. AI-powered energy techniques 171 7.4. Case studies 173 7.
5. Prospects for the future 175 7.6. Conclusion 176 7.7. References 177 Chapter 8. Pathways to Net-Zero: Challenges, Risks and the Future of AI in Sustainable Energy 179 Mushtaq Ahmad RATHER and Vatika JALALI 8.1.
Introduction: AI and the data race to net-zero. 179 8.2. The dual impact of AI on energy emissions 181 8.3. Four AI-driven transition pathways to net-zero. 184 8.4.
Systemic risks in the AI-powered energy transition 189 8.5. Governance stack for responsible AI in energy 194 8.6. Conclusion: co-producing a livable climate with code 200 8.7. References 201 Chapter 9. The Future of AI, ML and DL in Energy and Sustainability 205 Mamta NARWARIA, Sarika AGARWAL, Renu MISHRA, Aman KUMAR, Avinash CHAUHAN and Ramneet 9.
1. Introduction 206 9.2. Overview of AI, ML and DL in the context of energy 207 9.3. Functions of AI, ML and DL in energy systems 209 9.4. AI and ML for sustainability goals 213 9.
5. Case studies and key findings 215 9.6. Challenges and limitations in AI, ML and DL for energy and sustainability 216 9.7. Emerging trends and future directions 219 9.8. Conclusion 221 9.
9. References 223 Chapter 10. Hydrogen Intelligence (HyAI): A Data-Driven Approach to Transforming the Global Hydrogen Ecosystem 227 Deepak KUMAR and Shaman SHARMA 10.1. Introduction 228 10.2. Literature review 231 10.3.
Methodology: the HyAI framework 234 10.4. Results and discussion 241 10.5. Case study and model evaluation 242 10.6. Summary 245 10.7.
References 247 List of Authors 253 Index 257.