Chapter 1. AI-Driven Advanced Smart Grid with Optimized Hybrid Renewable Energy Systems 1
Mary A.G. EZHIL, S. JAISIVA, M. SUTHANTHIRA, R. ANUJA, M. Dhiviya NYCIL and A.S. MONIKANDAN

1.1. Introduction 1
1.2. Overview of renewable energy systems 2
1.3. Evolution phases of AI in hybrid renewable energy systems 8
1.4. Integration of AI in hybrid energy systems 10
1.5. Analyzing the integration of AI models in renewable energy systems 16
1.6. AI-optimized hybrid system design 21
1.7. Performance metrics and evaluation 25
1.8. Challenges and future directions 28
1.9. Conclusion 29
1.10. References 31

Chapter 2. Implementation of an AI-Driven Hybrid Renewable Energy Management System Using Deep Fuzzy-Based Particle Swarm Optimization (DFB-PSO) 33
E. Afreen BANU, Rajasekaran PALANIAPPAN, J.D. Dorathi JAYASEELI and P. ROBERT

2.1. Introduction 33
2.2. Review of optimization algorithms in hybrid renewable energy systems 37
2.3. Deep learning, fuzzy logic and swarm intelligence hybrid AI solutions 39
2.4. Architecture and methodology 42
2.5. Implementation process 45
2.6. Results and discussion 48
2.7. Conclusion 53
2.8. References 54

Chapter 3. Generative AI for Hybrid Renewable Energy Systems (Solar–Wind–Hydro Integration) 57
Mamta

3.1. Introduction 57
3.2. Literature review 60
3.3. Fundamentals of generative AI in hybrid systems 62
3.4. Proposed framework and methodology 65
3.5. Case study/experimental analysis 68
3.6. Results and discussion 71
3.7. Challenges and limitations 74
3.8. Future directions 75
3.9. Conclusion 76
3.10. References 77

Chapter 4. AI for Enhancing Sustainability in Wind Energy 81
Komal MISHRA and Suman CHAHAR

4.1. Introduction 81
4.2. Difficulties related to the sustainability of wind energy 83
4.3. Brief explanation of AI techniques 85
4.4. Using AI to find and select wind sites 87
4.5. Using AI in predictive maintenance 88
4.6. Intelligent control systems for wind turbines 90
4.7. Forecasting wind power through the use of machine learning 91
4.8. AI for linking different energy sources and ensuring management 93
4.9. Challenges, limitations and ethical considerations 94
4.10. Future outlook and research directions 95
4.11. Conclusion 98
4.12. References 99

Chapter 5. Intelligent Energy with AI-Driven Innovations in Wind Power Systems 101
R. RAJASREE, D. LAKSHMI and Malathy BATUMALAY

5.1. Introduction 101
5.2. Literature review 105
5.3. Proposed methodology 112
5.4. Results and discussion 117
5.5. Conclusion and future work. 122
5.6. References 123

Chapter 6. Economic and Market Impacts of AI in Wind Power 127
Mantena Siva Pavan Kumar RAJU and Mantena SIREESHA

6.1. Introduction 127
6.2. Operational efficiencies and cost savings through AI 130
6.3. Influence of AI on market dynamics 132
6.4. Economic analysis of AI integration in wind projects 134
6.5. Role of AI in wind power financing and investment trends 137
6.6. Limitations and challenges 139
6.7. Future opportunities 141
6.8. Conclusion 143
6.9. References 144

Chapter 7. AI for Policy and Regulatory Frameworks in Wind Power 151
Suman CHAHAR and Komal MISHRA

7.1. Introduction 151
7.2. Challenges in current policy and regulatory frameworks 152
7.3. Overview of AI technologies relevant to policy and regulation 156
7.4. Applications of AI in wind power policy and regulation 161
7.5. Case studies and global best practices 166
7.6. Future direction 168
7.7. Conclusion 171
7.8. References 172

Chapter 8. Implementation of an AI-Driven Wind Energy Sustainability Framework Using Reinforcement Learning-Optimized Deep Neuro-Fuzzy Controller (RL-DNFC) 175
Rajasekaran PALANIAPPAN, E. Afreen BANU, P. ROBERT and J.D. Dorathi JAYASEELI

8.1. Introduction 175
8.2. Literature review 177
8.3. Wind energy control using fuzzy logic 177
8.4. Neural and deep learning models to predict wind power 178
8.5. Adaptive wind energy control with reinforcement learning 178
8.6. Neuro-fuzzy and hybrid reinforcement approaches 179
8.7. System architecture of the AI-driven wind energy sustainability framework 180
8.8. Methodology and algorithmic design 183
8.9. Implementation setup and simulation environment 187
8.10. Experimental results and performance analysis 190
8.11. Discussion 196
8.12. Conclusion 200
8.13. References 201

Chapter 9. AI in Offshore Wind Energy Systems 203
V. VANITHA and M. YASHICA

9.1. Overview of offshore wind energy 204
9.2. AI in offshore wind farms 204
9.3. Case studies 211
9.4. Challenges and future trends 213
9.5. Conclusion 214
9.6. References 215

Chapter 10. Emerging AI Innovations in Wind Power 217
R. GAYATHRI and V.VANITHA

10.1. Introduction 218
10.2. Applications of AI in the wind industry 220
10.3. Case studies 231
10.4. Challenges of AI in the wind industry 235
10.5. Conclusion 238
10.6. References 238

Chapter 11. Generative AI for Energy Consumption Behavior Analysis 241
S. VANSHIKA and Neetu RANI

11.1. Introduction 242
11.2. Fundamentals of generative AI for energy consumption behavior 243
11.3. Data in energy behavior analysis 247
11.4. Applications of generative AI in energy consumption analysis 249
11.5. Case studies 253
11.6. Challenges and ethical considerations 256
11.7. Conclusion 257
11.8. References 258

Chapter 12. Wind Power Forecasting for Grid Stability Enhancement with Effective Integration of AI Techniques 261
J. Johncy BAI, A. Lelin FRED, S. Jaisiva, V. VELMURUGAN and T. Dharma RAJ

12.1. Introduction 261
12.2. Overview of wind power prediction 264
12.3. Workflow of wind power prediction 272
12.4. Grid stability 284
12.5. Conclusion 287
12.6. References 288

List of Authors 291
Index 295