Inam Ul Haq is an assistant professor at the CGC University, Mohali, India. His research specializes in AI, quantum computing, renewable energy analytics, medical research and integrating AI-driven solutions across engineering and healthcare domains.Sanna Mehraj Kak is an assistant professor at the Noida Institute of Engineering and Technology, India. Her research specializes in AI, cybersecurity and cloud computing.Anand Kumar Gupta is a professor and head of CSE (AI) at the Noida Institute of Engineering and Technology, India. His research specializes in AI, ML, DL, social network analysis, NLP and link prediction.Muhammad Sher Ramzan is a professor at the Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Saudi Arabia. His research specializes in information security, next-generation networks, information systems, ubiquitous and smart computing, and cloud computing.

• Preface xvInam UL HAQ, Sanna Mehraj KAK, Anand Kumar GUPTA and Muhammad Sher RAMZANChapter 1. Introduction to Sustainable Energy Systems 1Inam UL HAQ1.1. Introduction 11.2. Components of sustainable energy systems 41.3. Sustainability issues in energy 71.4. AI in sustainable energy systems 81.5. Case studies and applications 111.6. Future directions 131.7. Conclusion 141.8. References 15Chapter 2. Overview of AI, ML and DL Applications in Energy and Sustainability 17Priya PANDEY, Ashish PANDEY and Hema MAHAWAR2.1. Introduction 172.2. Literature review 252.3. Methodology 272.4. Challenges 302.5. Conclusion and future scope 322.6. References 33Chapter 3. Overview of AI, ML and DL Applications in Energy and Sustainability 37Mrinal PANDEY and Monika GOYAL3.1. Introduction 373.2. Literature survey 393.3. Brief description of AI, ML and DL 403.4. Applications of AI in energy sectors and sustainability 423.5. Case study 453.6. Conclusion 493.7. References 49Chapter 4. Conceptual and Methodological Insights into AI, ML and DL for Energy and Environmental Challenges 51Inderdeep KAUR4.1. Introduction 524.2. Conceptual foundations of AI, ML and DL 574.3. Methodologies for environmental data acquisition 644.4. AI-based predictive modeling for environmental challenges 684.5. AI-driven energy system optimization 734.6. Smart agriculture and precision forestry 764.7. Challenges and ethical considerations 784.8. Future directions and emerging trends 814.9. Conclusion 834.10. References 84Chapter 5. AI in Renewable Energy Technologies 89Jagdeep KAUR, Vidhi GUPTA and Aditi RAJ5.1. Introduction to sustainable energy systems 895.2. Introduction to AI in renewable energy technologies 945.3. Smart grids, IoT and AI for energy management 995.4. Global policies and AI-enabled energy strategies 1065.5. Future directions, pathways to net-zero: challenges, risks and future of AI in sustainable energy 1145.6. Conclusion 1185.7. References 119Chapter 6. Big Data Analytics and Predictive Models in Energy Systems 121Biswajit DAS, Himanshu PABBI, Shweta SINGH, Monika MEHRA and Sanny KUMAR6.1. Introduction 1216.2. Big data analytics in energy systems basics 1266.3. Predictive modeling: concepts and techniques 1316.4. Applications of big data analytics in energy systems 1406.5. Integration of IoT, Edge and cloud technologies 1446.6. Case studies and real-world implementations 1476.7. Challenges, limitations and future directions 1506.8. Conclusion 1546.9. References 156Chapter 7. Smart Grids, IoT and AI: Transforming Energy Management for a Sustainable Future 161Khalid Hafiz MIR and Anzah BASHIR7.1. Introduction 1627.2. Ethical considerations 1647.3. Regulatory frameworks 1707.4. Metrics for ethical and regulatory compliance 1727.5. Future directions 1737.6. Conclusion 1737.7. References 174Chapter 8. Digital Twins and AI for Predictive Maintenance of Renewable Energy Assets 177Abdul Malik ANSARI8.1. Introduction 1778.2. DTs in renewable energy 1798.3. System architecture of DT–AI integration 1838.4. Integration of DTs and AI 1858.5. Comparative analysis of maintenance strategies 1928.6. Conclusion 1948.7. References 194Chapter 9. Digitally Enhanced Fire Alarm System Using Sensor Driven Arduino Implementation for Smart Energy Management 197Ranjit Kumar BINDAL and Akhil NIGAM9.1. Introduction 1989.2. Literature review 1989.3. Problem formulation 2019.4. Constraints 2029.5. Advantages of Arduino Uno over other types of Arduino modules 2079.6. Concluding remarks 2109.7. References 211Chapter 10. AI and Blockchain for Renewable Energy Trading 215Hitendra SINGH, Pradeep Kumar SHARMA, Deepti GUPTA, Fardeen Ahmad KHAN, Bandana KUMARI, Shivani SHARMA, Prashant KUMAR and Sanny KUMAR10.1. Introduction 21510.2. AI in renewable energy 21810.3. Renewable energy with blockchain 22010.4. AI–blockchain synergy 22610.5. Challenges and limitations in blockchain and AI-based renewable energy trading 23010.6. Future prospects 23310.7. Summary 23610.8. References 237Chapter 11. Smart Energy Grids: Architecture, Security and Emerging Technologies 241Dhruv GOEL, Pratham KUMAR, Mamta NARWARIA and Md Jauhar IMAM11.1. Introduction 24111.2. Literature review/background 24211.3. IoT-enabled smart energy grid framework (methodology) 24511.4. Results and discussion 24811.5. Security vulnerabilities and threat models 24911.6. Challenges and future scope 25211.7. Conclusion 25411.8. References 255List of Authors 257Index 261