Abhishek Kumar is a senior IEEE member, and an assistant director and professor in the Department of Computer Science and Engineering at Chandigarh University, India.Ananth Kumar T. is a senior IEEE member, and an associate professor and Head of the Department of Computer Science and Engineering at the IFET College of Engineering (Autonomous Institution), Tamil Nadu, India.Ashutosh Kumar Dubey is an associate professor in the Department of Computer Science at the School of Engineering and Technology, Chitkara University, India.Arun Lal Srivastav is an associate professor at the School of Engineering and Technology, Chitkara University, Himachal Pradesh, India.J. Reyes Juárez-Ramírez is a professor–researcher in the Facultad de Ciencias Quíquimicas e Ingeniería, Universidad Autónoma de Baja California, Mexico.

• Preface xvAbhishek KUMAR, Ananth Kumar T., Ashutosh Kumar DUBEY, Arun Lal SRIVASTAV and J. Reyes JUÁREZ-RAMÍREZChapter 1. AI-Driven Advanced Smart Grid with Optimized Hybrid Renewable Energy Systems 1Mary A.G. EZHIL, S. JAISIVA, M. SUTHANTHIRA, R. ANUJA, M. Dhiviya NYCIL and A.S. MONIKANDAN1.1. Introduction 11.2. Overview of renewable energy systems 21.3. Evolution phases of AI in hybrid renewable energy systems 81.4. Integration of AI in hybrid energy systems 101.5. Analyzing the integration of AI models in renewable energy systems 161.6. AI-optimized hybrid system design 211.7. Performance metrics and evaluation 251.8. Challenges and future directions 281.9. Conclusion 291.10. References 31Chapter 2. Implementation of an AI-Driven Hybrid Renewable Energy Management System Using Deep Fuzzy-Based Particle Swarm Optimization (DFB-PSO) 33E. Afreen BANU, Rajasekaran PALANIAPPAN, J.D. Dorathi JAYASEELI and P. ROBERT2.1. Introduction 332.2. Review of optimization algorithms in hybrid renewable energy systems 372.3. Deep learning, fuzzy logic and swarm intelligence hybrid AI solutions 392.4. Architecture and methodology 422.5. Implementation process 452.6. Results and discussion 482.7. Conclusion 532.8. References 54Chapter 3. Generative AI for Hybrid Renewable Energy Systems (Solar–Wind–Hydro Integration) 57Mamta3.1. Introduction 573.2. Literature review 603.3. Fundamentals of generative AI in hybrid systems 623.4. Proposed framework and methodology 653.5. Case study/experimental analysis 683.6. Results and discussion 713.7. Challenges and limitations 743.8. Future directions 753.9. Conclusion 763.10. References 77Chapter 4. AI for Enhancing Sustainability in Wind Energy 81Komal MISHRA and Suman CHAHAR4.1. Introduction 814.2. Difficulties related to the sustainability of wind energy 834.3. Brief explanation of AI techniques 854.4. Using AI to find and select wind sites 874.5. Using AI in predictive maintenance 884.6. Intelligent control systems for wind turbines 904.7. Forecasting wind power through the use of machine learning 914.8. AI for linking different energy sources and ensuring management 934.9. Challenges, limitations and ethical considerations 944.10. Future outlook and research directions 954.11. Conclusion 984.12. References 99Chapter 5. Intelligent Energy with AI-Driven Innovations in Wind Power Systems 101R. RAJASREE, D. LAKSHMI and Malathy BATUMALAY5.1. Introduction 1015.2. Literature review 1055.3. Proposed methodology 1125.4. Results and discussion 1175.5. Conclusion and future work. 1225.6. References 123Chapter 6. Economic and Market Impacts of AI in Wind Power 127Mantena Siva Pavan Kumar RAJU and Mantena SIREESHA6.1. Introduction 1276.2. Operational efficiencies and cost savings through AI 1306.3. Influence of AI on market dynamics 1326.4. Economic analysis of AI integration in wind projects 1346.5. Role of AI in wind power financing and investment trends 1376.6. Limitations and challenges 1396.7. Future opportunities 1416.8. Conclusion 1436.9. References 144Chapter 7. AI for Policy and Regulatory Frameworks in Wind Power 151Suman CHAHAR and Komal MISHRA7.1. Introduction 1517.2. Challenges in current policy and regulatory frameworks 1527.3. Overview of AI technologies relevant to policy and regulation 1567.4. Applications of AI in wind power policy and regulation 1617.5. Case studies and global best practices 1667.6. Future direction 1687.7. Conclusion 1717.8. References 172Chapter 8. Implementation of an AI-Driven Wind Energy Sustainability Framework Using Reinforcement Learning-Optimized Deep Neuro-Fuzzy Controller (RL-DNFC) 175Rajasekaran PALANIAPPAN, E. Afreen BANU, P. ROBERT and J.D. Dorathi JAYASEELI8.1. Introduction 1758.2. Literature review 1778.3. Wind energy control using fuzzy logic 1778.4. Neural and deep learning models to predict wind power 1788.5. Adaptive wind energy control with reinforcement learning 1788.6. Neuro-fuzzy and hybrid reinforcement approaches 1798.7. System architecture of the AI-driven wind energy sustainability framework 1808.8. Methodology and algorithmic design 1838.9. Implementation setup and simulation environment 1878.10. Experimental results and performance analysis 1908.11. Discussion 1968.12. Conclusion 2008.13. References 201Chapter 9. AI in Offshore Wind Energy Systems 203V. VANITHA and M. YASHICA9.1. Overview of offshore wind energy 2049.2. AI in offshore wind farms 2049.3. Case studies 2119.4. Challenges and future trends 2139.5. Conclusion 2149.6. References 215Chapter 10. Emerging AI Innovations in Wind Power 217R. GAYATHRI and V.VANITHA10.1. Introduction 21810.2. Applications of AI in the wind industry 22010.3. Case studies 23110.4. Challenges of AI in the wind industry 23510.5. Conclusion 23810.6. References 238Chapter 11. Generative AI for Energy Consumption Behavior Analysis 241S. VANSHIKA and Neetu RANI11.1. Introduction 24211.2. Fundamentals of generative AI for energy consumption behavior 24311.3. Data in energy behavior analysis 24711.4. Applications of generative AI in energy consumption analysis 24911.5. Case studies 25311.6. Challenges and ethical considerations 25611.7. Conclusion 25711.8. References 258Chapter 12. Wind Power Forecasting for Grid Stability Enhancement with Effective Integration of AI Techniques 261J. Johncy BAI, A. Lelin FRED, S. Jaisiva, V. VELMURUGAN and T. Dharma RAJ12.1. Introduction 26112.2. Overview of wind power prediction 26412.3. Workflow of wind power prediction 27212.4. Grid stability 28412.5. Conclusion 28712.6. References 288List of Authors 291Index 295

Abhishek Kumar is a senior IEEE member, and an assistant director and professor in the Department of Computer Science and Engineering at Chandigarh University, India.Ananth Kumar T. is a senior IEEE member, and an associate professor and Head of the Department of Computer Science and Engineering at the IFET College of Engineering (Autonomous Institution), Tamil Nadu, India.Ashutosh Kumar Dubey is an associate professor in the Department of Computer Science at the School of Engineering and Technology, Chitkara University, India.Arun Lal Srivastav is an associate professor at the School of Engineering and Technology, Chitkara University, Himachal Pradesh, India.J. Reyes Juárez-Ramírez is a professor–researcher in the Facultad de Ciencias Quíquimicas e Ingeniería, Universidad Autónoma de Baja California, Mexico.

• Preface xvAbhishek KUMAR, Ananth Kumar T., Ashutosh Kumar DUBEY, Arun Lal SRIVASTAV and J. Reyes JUÁREZ-RAMÍREZChapter 1. AI-Driven Advanced Smart Grid with Optimized Hybrid Renewable Energy Systems 1Mary A.G. EZHIL, S. JAISIVA, M. SUTHANTHIRA, R. ANUJA, M. Dhiviya NYCIL and A.S. MONIKANDAN1.1. Introduction 11.2. Overview of renewable energy systems 21.3. Evolution phases of AI in hybrid renewable energy systems 81.4. Integration of AI in hybrid energy systems 101.5. Analyzing the integration of AI models in renewable energy systems 161.6. AI-optimized hybrid system design 211.7. Performance metrics and evaluation 251.8. Challenges and future directions 281.9. Conclusion 291.10. References 31Chapter 2. Implementation of an AI-Driven Hybrid Renewable Energy Management System Using Deep Fuzzy-Based Particle Swarm Optimization (DFB-PSO) 33E. Afreen BANU, Rajasekaran PALANIAPPAN, J.D. Dorathi JAYASEELI and P. ROBERT2.1. Introduction 332.2. Review of optimization algorithms in hybrid renewable energy systems 372.3. Deep learning, fuzzy logic and swarm intelligence hybrid AI solutions 392.4. Architecture and methodology 422.5. Implementation process 452.6. Results and discussion 482.7. Conclusion 532.8. References 54Chapter 3. Generative AI for Hybrid Renewable Energy Systems (Solar–Wind–Hydro Integration) 57Mamta3.1. Introduction 573.2. Literature review 603.3. Fundamentals of generative AI in hybrid systems 623.4. Proposed framework and methodology 653.5. Case study/experimental analysis 683.6. Results and discussion 713.7. Challenges and limitations 743.8. Future directions 753.9. Conclusion 763.10. References 77Chapter 4. AI for Enhancing Sustainability in Wind Energy 81Komal MISHRA and Suman CHAHAR4.1. Introduction 814.2. Difficulties related to the sustainability of wind energy 834.3. Brief explanation of AI techniques 854.4. Using AI to find and select wind sites 874.5. Using AI in predictive maintenance 884.6. Intelligent control systems for wind turbines 904.7. Forecasting wind power through the use of machine learning 914.8. AI for linking different energy sources and ensuring management 934.9. Challenges, limitations and ethical considerations 944.10. Future outlook and research directions 954.11. Conclusion 984.12. References 99Chapter 5. Intelligent Energy with AI-Driven Innovations in Wind Power Systems 101R. RAJASREE, D. LAKSHMI and Malathy BATUMALAY5.1. Introduction 1015.2. Literature review 1055.3. Proposed methodology 1125.4. Results and discussion 1175.5. Conclusion and future work. 1225.6. References 123Chapter 6. Economic and Market Impacts of AI in Wind Power 127Mantena Siva Pavan Kumar RAJU and Mantena SIREESHA6.1. Introduction 1276.2. Operational efficiencies and cost savings through AI 1306.3. Influence of AI on market dynamics 1326.4. Economic analysis of AI integration in wind projects 1346.5. Role of AI in wind power financing and investment trends 1376.6. Limitations and challenges 1396.7. Future opportunities 1416.8. Conclusion 1436.9. References 144Chapter 7. AI for Policy and Regulatory Frameworks in Wind Power 151Suman CHAHAR and Komal MISHRA7.1. Introduction 1517.2. Challenges in current policy and regulatory frameworks 1527.3. Overview of AI technologies relevant to policy and regulation 1567.4. Applications of AI in wind power policy and regulation 1617.5. Case studies and global best practices 1667.6. Future direction 1687.7. Conclusion 1717.8. References 172Chapter 8. Implementation of an AI-Driven Wind Energy Sustainability Framework Using Reinforcement Learning-Optimized Deep Neuro-Fuzzy Controller (RL-DNFC) 175Rajasekaran PALANIAPPAN, E. Afreen BANU, P. ROBERT and J.D. Dorathi JAYASEELI8.1. Introduction 1758.2. Literature review 1778.3. Wind energy control using fuzzy logic 1778.4. Neural and deep learning models to predict wind power 1788.5. Adaptive wind energy control with reinforcement learning 1788.6. Neuro-fuzzy and hybrid reinforcement approaches 1798.7. System architecture of the AI-driven wind energy sustainability framework 1808.8. Methodology and algorithmic design 1838.9. Implementation setup and simulation environment 1878.10. Experimental results and performance analysis 1908.11. Discussion 1968.12. Conclusion 2008.13. References 201Chapter 9. AI in Offshore Wind Energy Systems 203V. VANITHA and M. YASHICA9.1. Overview of offshore wind energy 2049.2. AI in offshore wind farms 2049.3. Case studies 2119.4. Challenges and future trends 2139.5. Conclusion 2149.6. References 215Chapter 10. Emerging AI Innovations in Wind Power 217R. GAYATHRI and V.VANITHA10.1. Introduction 21810.2. Applications of AI in the wind industry 22010.3. Case studies 23110.4. Challenges of AI in the wind industry 23510.5. Conclusion 23810.6. References 238Chapter 11. Generative AI for Energy Consumption Behavior Analysis 241S. VANSHIKA and Neetu RANI11.1. Introduction 24211.2. Fundamentals of generative AI for energy consumption behavior 24311.3. Data in energy behavior analysis 24711.4. Applications of generative AI in energy consumption analysis 24911.5. Case studies 25311.6. Challenges and ethical considerations 25611.7. Conclusion 25711.8. References 258Chapter 12. Wind Power Forecasting for Grid Stability Enhancement with Effective Integration of AI Techniques 261J. Johncy BAI, A. Lelin FRED, S. Jaisiva, V. VELMURUGAN and T. Dharma RAJ12.1. Introduction 26112.2. Overview of wind power prediction 26412.3. Workflow of wind power prediction 27212.4. Grid stability 28412.5. Conclusion 28712.6. References 288List of Authors 291Index 295

Abhishek Kumar is a senior IEEE member, and an assistant director and professor in the Department of Computer Science and Engineering at Chandigarh University, India.Ananth Kumar T. is a senior IEEE member, and an associate professor and Head of the Department of Computer Science and Engineering at the IFET College of Engineering (Autonomous Institution), Tamil Nadu, India.Ashutosh Kumar Dubey is an associate professor in the Department of Computer Science at the School of Engineering and Technology, Chitkara University, India.Arun Lal Srivastav is an associate professor at the School of Engineering and Technology, Chitkara University, Himachal Pradesh, India.J. Reyes Juárez-Ramírez is a professor–researcher in the Facultad de Ciencias Quíquimicas e Ingeniería, Universidad Autónoma de Baja California, Mexico.

• Preface xvAbhishek KUMAR, Ananth Kumar T., Ashutosh Kumar DUBEY, Arun Lal SRIVASTAV and J. Reyes JUÁREZ-RAMÍREZChapter 1. AI-Driven Advanced Smart Grid with Optimized Hybrid Renewable Energy Systems 1Mary A.G. EZHIL, S. JAISIVA, M. SUTHANTHIRA, R. ANUJA, M. Dhiviya NYCIL and A.S. MONIKANDAN1.1. Introduction 11.2. Overview of renewable energy systems 21.3. Evolution phases of AI in hybrid renewable energy systems 81.4. Integration of AI in hybrid energy systems 101.5. Analyzing the integration of AI models in renewable energy systems 161.6. AI-optimized hybrid system design 211.7. Performance metrics and evaluation 251.8. Challenges and future directions 281.9. Conclusion 291.10. References 31Chapter 2. Implementation of an AI-Driven Hybrid Renewable Energy Management System Using Deep Fuzzy-Based Particle Swarm Optimization (DFB-PSO) 33E. Afreen BANU, Rajasekaran PALANIAPPAN, J.D. Dorathi JAYASEELI and P. ROBERT2.1. Introduction 332.2. Review of optimization algorithms in hybrid renewable energy systems 372.3. Deep learning, fuzzy logic and swarm intelligence hybrid AI solutions 392.4. Architecture and methodology 422.5. Implementation process 452.6. Results and discussion 482.7. Conclusion 532.8. References 54Chapter 3. Generative AI for Hybrid Renewable Energy Systems (Solar–Wind–Hydro Integration) 57Mamta3.1. Introduction 573.2. Literature review 603.3. Fundamentals of generative AI in hybrid systems 623.4. Proposed framework and methodology 653.5. Case study/experimental analysis 683.6. Results and discussion 713.7. Challenges and limitations 743.8. Future directions 753.9. Conclusion 763.10. References 77Chapter 4. AI for Enhancing Sustainability in Wind Energy 81Komal MISHRA and Suman CHAHAR4.1. Introduction 814.2. Difficulties related to the sustainability of wind energy 834.3. Brief explanation of AI techniques 854.4. Using AI to find and select wind sites 874.5. Using AI in predictive maintenance 884.6. Intelligent control systems for wind turbines 904.7. Forecasting wind power through the use of machine learning 914.8. AI for linking different energy sources and ensuring management 934.9. Challenges, limitations and ethical considerations 944.10. Future outlook and research directions 954.11. Conclusion 984.12. References 99Chapter 5. Intelligent Energy with AI-Driven Innovations in Wind Power Systems 101R. RAJASREE, D. LAKSHMI and Malathy BATUMALAY5.1. Introduction 1015.2. Literature review 1055.3. Proposed methodology 1125.4. Results and discussion 1175.5. Conclusion and future work. 1225.6. References 123Chapter 6. Economic and Market Impacts of AI in Wind Power 127Mantena Siva Pavan Kumar RAJU and Mantena SIREESHA6.1. Introduction 1276.2. Operational efficiencies and cost savings through AI 1306.3. Influence of AI on market dynamics 1326.4. Economic analysis of AI integration in wind projects 1346.5. Role of AI in wind power financing and investment trends 1376.6. Limitations and challenges 1396.7. Future opportunities 1416.8. Conclusion 1436.9. References 144Chapter 7. AI for Policy and Regulatory Frameworks in Wind Power 151Suman CHAHAR and Komal MISHRA7.1. Introduction 1517.2. Challenges in current policy and regulatory frameworks 1527.3. Overview of AI technologies relevant to policy and regulation 1567.4. Applications of AI in wind power policy and regulation 1617.5. Case studies and global best practices 1667.6. Future direction 1687.7. Conclusion 1717.8. References 172Chapter 8. Implementation of an AI-Driven Wind Energy Sustainability Framework Using Reinforcement Learning-Optimized Deep Neuro-Fuzzy Controller (RL-DNFC) 175Rajasekaran PALANIAPPAN, E. Afreen BANU, P. ROBERT and J.D. Dorathi JAYASEELI8.1. Introduction 1758.2. Literature review 1778.3. Wind energy control using fuzzy logic 1778.4. Neural and deep learning models to predict wind power 1788.5. Adaptive wind energy control with reinforcement learning 1788.6. Neuro-fuzzy and hybrid reinforcement approaches 1798.7. System architecture of the AI-driven wind energy sustainability framework 1808.8. Methodology and algorithmic design 1838.9. Implementation setup and simulation environment 1878.10. Experimental results and performance analysis 1908.11. Discussion 1968.12. Conclusion 2008.13. References 201Chapter 9. AI in Offshore Wind Energy Systems 203V. VANITHA and M. YASHICA9.1. Overview of offshore wind energy 2049.2. AI in offshore wind farms 2049.3. Case studies 2119.4. Challenges and future trends 2139.5. Conclusion 2149.6. References 215Chapter 10. Emerging AI Innovations in Wind Power 217R. GAYATHRI and V.VANITHA10.1. Introduction 21810.2. Applications of AI in the wind industry 22010.3. Case studies 23110.4. Challenges of AI in the wind industry 23510.5. Conclusion 23810.6. References 238Chapter 11. Generative AI for Energy Consumption Behavior Analysis 241S. VANSHIKA and Neetu RANI11.1. Introduction 24211.2. Fundamentals of generative AI for energy consumption behavior 24311.3. Data in energy behavior analysis 24711.4. Applications of generative AI in energy consumption analysis 24911.5. Case studies 25311.6. Challenges and ethical considerations 25611.7. Conclusion 25711.8. References 258Chapter 12. Wind Power Forecasting for Grid Stability Enhancement with Effective Integration of AI Techniques 261J. Johncy BAI, A. Lelin FRED, S. Jaisiva, V. VELMURUGAN and T. Dharma RAJ12.1. Introduction 26112.2. Overview of wind power prediction 26412.3. Workflow of wind power prediction 27212.4. Grid stability 28412.5. Conclusion 28712.6. References 288List of Authors 291Index 295