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. Harnessing the Power of the Wind: A Detailed Exploration of Wind Energy Fundamentals and the Pivotal Role of Emerging AI Techniques 1R. VENKATESH and D. VETRITHANGAM1.1. Introduction to wind energy 21.2. Fundamentals of wind energy generation 61.3. Wind energy systems and grid integration 121.4. Emerging AI techniques in wind energy 141.5. Predictive maintenance and performance monitoring 171.6. AI in wind farm design and layout optimization 211.7. Energy forecasting and demand response with AI 241.8. Future trends and innovations in wind energy and AI 261.9. Conclusion 301.10. References 32Chapter 2. A Terrain-Fused Spatio-Temporal Deep Learning Framework for Accurate Wind Resource Assessment and Forecasting Using TFS-TWF 35Suresh Kumar K., Kirubha Sagar T., Arun Prakash P. and Praveen Kumar M.2.1. Introduction 362.2. Literature survey 382.3. Proposed work 422.4. TFS-TWF proposed architecture 432.5. Implementation and methodology 452.6. Results and discussion 472.7. Performance in Region C's hilly terrain 492.8. Projected horizon performance 492.9. Study of ablation 502.10. Estimating uncertainty and dependability 512.11. Summary of comparative performance 522.12. Prospects and remarks 522.13. Conclusion 532.14. References 54Chapter 3. Deep Fuzzy-Optimized CLSTM–BERT Algorithm with Adaptive Learning for Efficient Wind Farm Design and Energy Management 57Prabbu Sankar P., Yaashuwanth C., Prathibanandhi K. and S. RAMESH3.1. Introduction 583.2. Literature review 613.3. Proposed methodology 663.4. Results and discussion 733.5. Conclusion 793.6. References 80Chapter 4. Optimizing Wind Energy with AI 83Harshvardhan KUNWAR, Shruti ROY, Ahanya BANERJI and Sayantika MUKHERJEE4.1. Introduction 834.2. Literature review 854.3. Methodology 884.5. Case studies and real-world applications 944.6. AI versus traditional methods: what is the difference? 974.7. Challenges and limitations of using AI in wind energy 984.8. Future outlook and recommendations 1004.9. Scalability in developing countries 1024.10. Conclusion 1044.11. References 105Chapter 5. Integration of Artificial Intelligence and Wind Power: An Orientation Technique. 109Johncy Bai J., T.S. SIVARANI, S. JAISIVA, Srividhya J.P. and Gayathri A.R.5.1. Introduction 1095.2. Introduction to wind power 1105.3. Overview of AI in renewable energy 1235.4. AI applications in wind power 1245.5. Popular AI techniques in wind power 1275.6. Challenges of AI in wind power 1285.7. Future trends and directions for research 1295.8. Conclusion 1355.9. References 136Chapter 6. Predictive Maintenance and Fault Diagnosis of Wind Turbines Using AI 139Karthick Manoj R. and Aasha Nandhini S.6.1. Introduction 1396.2. Related work 1426.3. Methodology 1456.4. Results and discussion 1506.5. Conclusion and future work 1556.6. References 156Chapter 7. A Comprehensive Review of Digital Twin-Enabled AI Models for Interpretable and Scalable Fault Diagnosis in Wind Turbines. 159N.C. DESAI and Priyanka P. SHINDE7.1. Introduction 1597.2. Overview of wind turbine fault diagnosis 1607.3. Digital twin technology: concept and role in wind turbines 1627.4. AI techniques in fault diagnosis 1647.5. Integration of digital twin and AI: synergies and architectures 1667.6. Explainable AI for interpretability 1697.7. Sensor fusion and multimodal data integration 1707.8. Uncertainty quantification in AI models 1727.9. Validation strategies and simulation frameworks 1757.10. Scalability considerations for large wind farms 1777.11. Open challenges and future directions 1797.12. Conclusion 1817.13. References 182Chapter 8. A Hybrid AI-Driven Digital Twin Technology for Predictive Maintenance and Fault Diagnosis in Wind Turbines under Variable Environmental Conditions using EcoHyTwin Framework 187Mahesh Kumar S., Suresh Kumar K., Arun Prakash P. and Karthick Raja M.8.1. Introduction 1888.2. Literature survey 1918.3. Proposed work 1958.4. Algorithm, implementation and expected outcomes 1988.5. Dataset and experimental setup 1998.6. Fault detection accuracy 1998.7. RUL prediction 2008.8. Environmental adaptability performance 2018.9. Fault-type classification performance 2018.10. Energy efficiency and maintenance cost reduction 2028.11. Hybrid AI learning efficiency 2028.12. Comparative analysis with existing approaches 2038.13. Discussion 2048.14. Conclusion 2048.15. References 205Chapter 9. An AI-Driven Framework for Predictive Maintenance and Adaptive Control in Hybrid Wind Farms Using BreezeSenseAI Algorithm 209Suresh Kumar K., Karthikesh N., Anandaraj A. and Yuvaraj S.9.1. Introduction 2109.2. Overview of the BreezeSenseAI framework 2169.3. Methodology and system architecture 2179.4. Implementation, evaluation and novel contributions 2189.5. Adaptive control optimization 2219.6. Turbine health index (THI) evaluation 2229.7. Energy efficiency and cost reduction 2229.8. Real-time adaptability evaluation 2239.9. Comparative analysis with existing models 2239.10. Conclusion 2249.11. References 224Chapter 10. AI-Powered Predictive Maintenance for Wind Energy. 227Anurag WAZARKAR, Pratik GUNJALKAR, Tanmay SAWANT, Sanket BABAR, Bhushan S. YELURE and Priyanka P. SHINDE10.1. Introduction 22710.2. Fundamentals of wind turbine systems and failure modes 22810.3. Predictive maintenance (PdM) techniques for wind turbines 23010.4. AI for predictive maintenance and fault diagnosis 23410.5. Challenges and future directions 23810.6. Conclusion 24010.7. References 241Chapter 11. Integrating AI and IoT Technologies in Smart Wind Farms: Leveraging Predictive Maintenance and Performance Optimization. 245Sathish Kumar D., Vanitha U., Saravanan G. and Ramya G.11.1. Introduction to smart wind farms 24511.2. Challenges in traditional wind farm operations 24811.3. Role of smart technologies in the energy sector 25011.4. Overview of smart wind farms 25111.5. Components of smart wind farms 25111.6. Internet of Things (IoT) in wind farms 25311.7. Cloud-centric IoT architecture 25411.8. Edge-centric IoT architecture 25511.9. Hybrid IoT architecture 25711.10. Sensor deployment for real-time monitoring in wind farms 25811.11. Data acquisition and wireless communication in wind farms 26011.12. Cloud and edge computing for data processing in smart farms 26111.13. AI applications in smart wind farms 26211.14. Performance prediction and optimization algorithms 26411.15. Case studies and real-world implementations 26611.16. Conclusion 26911.17. References 269Chapter 12. AI and IoT for Smart Wind Farms 271Mantena SIREESHA and Mantena Siva Pavan Kumar RAJU12.1. Introduction 27112.2. Fundamentals of AI and IoT in wind farm operations 27312.3. IoT for data acquisition and real-time monitoring 27512.4. AI for predictive maintenance and fault detection 27712.5. Performance optimization and energy production forecasting 28012.6. Challenges and barriers to implementation 28212.7. Future trends and innovations in smart wind farms 28412.8. Conclusion 28512.9. References 287xiv AI and Wind Power 1Chapter 13. Intelligent Wind Power Forecasting and Demand-Responsive Power Scheduling Using LSTMs 293Pranav Raja K.R., Ritik and Karthika S.K.13.1. Introduction 29313.2. Literature review 29513.3. Methodology 30013.4. Results and analysis 31013.5. Conclusion and future work 31213.6. References 313List of Authors 317Index 323

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. Harnessing the Power of the Wind: A Detailed Exploration of Wind Energy Fundamentals and the Pivotal Role of Emerging AI Techniques 1R. VENKATESH and D. VETRITHANGAM1.1. Introduction to wind energy 21.2. Fundamentals of wind energy generation 61.3. Wind energy systems and grid integration 121.4. Emerging AI techniques in wind energy 141.5. Predictive maintenance and performance monitoring 171.6. AI in wind farm design and layout optimization 211.7. Energy forecasting and demand response with AI 241.8. Future trends and innovations in wind energy and AI 261.9. Conclusion 301.10. References 32Chapter 2. A Terrain-Fused Spatio-Temporal Deep Learning Framework for Accurate Wind Resource Assessment and Forecasting Using TFS-TWF 35Suresh Kumar K., Kirubha Sagar T., Arun Prakash P. and Praveen Kumar M.2.1. Introduction 362.2. Literature survey 382.3. Proposed work 422.4. TFS-TWF proposed architecture 432.5. Implementation and methodology 452.6. Results and discussion 472.7. Performance in Region C's hilly terrain 492.8. Projected horizon performance 492.9. Study of ablation 502.10. Estimating uncertainty and dependability 512.11. Summary of comparative performance 522.12. Prospects and remarks 522.13. Conclusion 532.14. References 54Chapter 3. Deep Fuzzy-Optimized CLSTM–BERT Algorithm with Adaptive Learning for Efficient Wind Farm Design and Energy Management 57Prabbu Sankar P., Yaashuwanth C., Prathibanandhi K. and S. RAMESH3.1. Introduction 583.2. Literature review 613.3. Proposed methodology 663.4. Results and discussion 733.5. Conclusion 793.6. References 80Chapter 4. Optimizing Wind Energy with AI 83Harshvardhan KUNWAR, Shruti ROY, Ahanya BANERJI and Sayantika MUKHERJEE4.1. Introduction 834.2. Literature review 854.3. Methodology 884.5. Case studies and real-world applications 944.6. AI versus traditional methods: what is the difference? 974.7. Challenges and limitations of using AI in wind energy 984.8. Future outlook and recommendations 1004.9. Scalability in developing countries 1024.10. Conclusion 1044.11. References 105Chapter 5. Integration of Artificial Intelligence and Wind Power: An Orientation Technique. 109Johncy Bai J., T.S. SIVARANI, S. JAISIVA, Srividhya J.P. and Gayathri A.R.5.1. Introduction 1095.2. Introduction to wind power 1105.3. Overview of AI in renewable energy 1235.4. AI applications in wind power 1245.5. Popular AI techniques in wind power 1275.6. Challenges of AI in wind power 1285.7. Future trends and directions for research 1295.8. Conclusion 1355.9. References 136Chapter 6. Predictive Maintenance and Fault Diagnosis of Wind Turbines Using AI 139Karthick Manoj R. and Aasha Nandhini S.6.1. Introduction 1396.2. Related work 1426.3. Methodology 1456.4. Results and discussion 1506.5. Conclusion and future work 1556.6. References 156Chapter 7. A Comprehensive Review of Digital Twin-Enabled AI Models for Interpretable and Scalable Fault Diagnosis in Wind Turbines. 159N.C. DESAI and Priyanka P. SHINDE7.1. Introduction 1597.2. Overview of wind turbine fault diagnosis 1607.3. Digital twin technology: concept and role in wind turbines 1627.4. AI techniques in fault diagnosis 1647.5. Integration of digital twin and AI: synergies and architectures 1667.6. Explainable AI for interpretability 1697.7. Sensor fusion and multimodal data integration 1707.8. Uncertainty quantification in AI models 1727.9. Validation strategies and simulation frameworks 1757.10. Scalability considerations for large wind farms 1777.11. Open challenges and future directions 1797.12. Conclusion 1817.13. References 182Chapter 8. A Hybrid AI-Driven Digital Twin Technology for Predictive Maintenance and Fault Diagnosis in Wind Turbines under Variable Environmental Conditions using EcoHyTwin Framework 187Mahesh Kumar S., Suresh Kumar K., Arun Prakash P. and Karthick Raja M.8.1. Introduction 1888.2. Literature survey 1918.3. Proposed work 1958.4. Algorithm, implementation and expected outcomes 1988.5. Dataset and experimental setup 1998.6. Fault detection accuracy 1998.7. RUL prediction 2008.8. Environmental adaptability performance 2018.9. Fault-type classification performance 2018.10. Energy efficiency and maintenance cost reduction 2028.11. Hybrid AI learning efficiency 2028.12. Comparative analysis with existing approaches 2038.13. Discussion 2048.14. Conclusion 2048.15. References 205Chapter 9. An AI-Driven Framework for Predictive Maintenance and Adaptive Control in Hybrid Wind Farms Using BreezeSenseAI Algorithm 209Suresh Kumar K., Karthikesh N., Anandaraj A. and Yuvaraj S.9.1. Introduction 2109.2. Overview of the BreezeSenseAI framework 2169.3. Methodology and system architecture 2179.4. Implementation, evaluation and novel contributions 2189.5. Adaptive control optimization 2219.6. Turbine health index (THI) evaluation 2229.7. Energy efficiency and cost reduction 2229.8. Real-time adaptability evaluation 2239.9. Comparative analysis with existing models 2239.10. Conclusion 2249.11. References 224Chapter 10. AI-Powered Predictive Maintenance for Wind Energy. 227Anurag WAZARKAR, Pratik GUNJALKAR, Tanmay SAWANT, Sanket BABAR, Bhushan S. YELURE and Priyanka P. SHINDE10.1. Introduction 22710.2. Fundamentals of wind turbine systems and failure modes 22810.3. Predictive maintenance (PdM) techniques for wind turbines 23010.4. AI for predictive maintenance and fault diagnosis 23410.5. Challenges and future directions 23810.6. Conclusion 24010.7. References 241Chapter 11. Integrating AI and IoT Technologies in Smart Wind Farms: Leveraging Predictive Maintenance and Performance Optimization. 245Sathish Kumar D., Vanitha U., Saravanan G. and Ramya G.11.1. Introduction to smart wind farms 24511.2. Challenges in traditional wind farm operations 24811.3. Role of smart technologies in the energy sector 25011.4. Overview of smart wind farms 25111.5. Components of smart wind farms 25111.6. Internet of Things (IoT) in wind farms 25311.7. Cloud-centric IoT architecture 25411.8. Edge-centric IoT architecture 25511.9. Hybrid IoT architecture 25711.10. Sensor deployment for real-time monitoring in wind farms 25811.11. Data acquisition and wireless communication in wind farms 26011.12. Cloud and edge computing for data processing in smart farms 26111.13. AI applications in smart wind farms 26211.14. Performance prediction and optimization algorithms 26411.15. Case studies and real-world implementations 26611.16. Conclusion 26911.17. References 269Chapter 12. AI and IoT for Smart Wind Farms 271Mantena SIREESHA and Mantena Siva Pavan Kumar RAJU12.1. Introduction 27112.2. Fundamentals of AI and IoT in wind farm operations 27312.3. IoT for data acquisition and real-time monitoring 27512.4. AI for predictive maintenance and fault detection 27712.5. Performance optimization and energy production forecasting 28012.6. Challenges and barriers to implementation 28212.7. Future trends and innovations in smart wind farms 28412.8. Conclusion 28512.9. References 287xiv AI and Wind Power 1Chapter 13. Intelligent Wind Power Forecasting and Demand-Responsive Power Scheduling Using LSTMs 293Pranav Raja K.R., Ritik and Karthika S.K.13.1. Introduction 29313.2. Literature review 29513.3. Methodology 30013.4. Results and analysis 31013.5. Conclusion and future work 31213.6. References 313List of Authors 317Index 323

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. Harnessing the Power of the Wind: A Detailed Exploration of Wind Energy Fundamentals and the Pivotal Role of Emerging AI Techniques 1R. VENKATESH and D. VETRITHANGAM1.1. Introduction to wind energy 21.2. Fundamentals of wind energy generation 61.3. Wind energy systems and grid integration 121.4. Emerging AI techniques in wind energy 141.5. Predictive maintenance and performance monitoring 171.6. AI in wind farm design and layout optimization 211.7. Energy forecasting and demand response with AI 241.8. Future trends and innovations in wind energy and AI 261.9. Conclusion 301.10. References 32Chapter 2. A Terrain-Fused Spatio-Temporal Deep Learning Framework for Accurate Wind Resource Assessment and Forecasting Using TFS-TWF 35Suresh Kumar K., Kirubha Sagar T., Arun Prakash P. and Praveen Kumar M.2.1. Introduction 362.2. Literature survey 382.3. Proposed work 422.4. TFS-TWF proposed architecture 432.5. Implementation and methodology 452.6. Results and discussion 472.7. Performance in Region C's hilly terrain 492.8. Projected horizon performance 492.9. Study of ablation 502.10. Estimating uncertainty and dependability 512.11. Summary of comparative performance 522.12. Prospects and remarks 522.13. Conclusion 532.14. References 54Chapter 3. Deep Fuzzy-Optimized CLSTM–BERT Algorithm with Adaptive Learning for Efficient Wind Farm Design and Energy Management 57Prabbu Sankar P., Yaashuwanth C., Prathibanandhi K. and S. RAMESH3.1. Introduction 583.2. Literature review 613.3. Proposed methodology 663.4. Results and discussion 733.5. Conclusion 793.6. References 80Chapter 4. Optimizing Wind Energy with AI 83Harshvardhan KUNWAR, Shruti ROY, Ahanya BANERJI and Sayantika MUKHERJEE4.1. Introduction 834.2. Literature review 854.3. Methodology 884.5. Case studies and real-world applications 944.6. AI versus traditional methods: what is the difference? 974.7. Challenges and limitations of using AI in wind energy 984.8. Future outlook and recommendations 1004.9. Scalability in developing countries 1024.10. Conclusion 1044.11. References 105Chapter 5. Integration of Artificial Intelligence and Wind Power: An Orientation Technique. 109Johncy Bai J., T.S. SIVARANI, S. JAISIVA, Srividhya J.P. and Gayathri A.R.5.1. Introduction 1095.2. Introduction to wind power 1105.3. Overview of AI in renewable energy 1235.4. AI applications in wind power 1245.5. Popular AI techniques in wind power 1275.6. Challenges of AI in wind power 1285.7. Future trends and directions for research 1295.8. Conclusion 1355.9. References 136Chapter 6. Predictive Maintenance and Fault Diagnosis of Wind Turbines Using AI 139Karthick Manoj R. and Aasha Nandhini S.6.1. Introduction 1396.2. Related work 1426.3. Methodology 1456.4. Results and discussion 1506.5. Conclusion and future work 1556.6. References 156Chapter 7. A Comprehensive Review of Digital Twin-Enabled AI Models for Interpretable and Scalable Fault Diagnosis in Wind Turbines. 159N.C. DESAI and Priyanka P. SHINDE7.1. Introduction 1597.2. Overview of wind turbine fault diagnosis 1607.3. Digital twin technology: concept and role in wind turbines 1627.4. AI techniques in fault diagnosis 1647.5. Integration of digital twin and AI: synergies and architectures 1667.6. Explainable AI for interpretability 1697.7. Sensor fusion and multimodal data integration 1707.8. Uncertainty quantification in AI models 1727.9. Validation strategies and simulation frameworks 1757.10. Scalability considerations for large wind farms 1777.11. Open challenges and future directions 1797.12. Conclusion 1817.13. References 182Chapter 8. A Hybrid AI-Driven Digital Twin Technology for Predictive Maintenance and Fault Diagnosis in Wind Turbines under Variable Environmental Conditions using EcoHyTwin Framework 187Mahesh Kumar S., Suresh Kumar K., Arun Prakash P. and Karthick Raja M.8.1. Introduction 1888.2. Literature survey 1918.3. Proposed work 1958.4. Algorithm, implementation and expected outcomes 1988.5. Dataset and experimental setup 1998.6. Fault detection accuracy 1998.7. RUL prediction 2008.8. Environmental adaptability performance 2018.9. Fault-type classification performance 2018.10. Energy efficiency and maintenance cost reduction 2028.11. Hybrid AI learning efficiency 2028.12. Comparative analysis with existing approaches 2038.13. Discussion 2048.14. Conclusion 2048.15. References 205Chapter 9. An AI-Driven Framework for Predictive Maintenance and Adaptive Control in Hybrid Wind Farms Using BreezeSenseAI Algorithm 209Suresh Kumar K., Karthikesh N., Anandaraj A. and Yuvaraj S.9.1. Introduction 2109.2. Overview of the BreezeSenseAI framework 2169.3. Methodology and system architecture 2179.4. Implementation, evaluation and novel contributions 2189.5. Adaptive control optimization 2219.6. Turbine health index (THI) evaluation 2229.7. Energy efficiency and cost reduction 2229.8. Real-time adaptability evaluation 2239.9. Comparative analysis with existing models 2239.10. Conclusion 2249.11. References 224Chapter 10. AI-Powered Predictive Maintenance for Wind Energy. 227Anurag WAZARKAR, Pratik GUNJALKAR, Tanmay SAWANT, Sanket BABAR, Bhushan S. YELURE and Priyanka P. SHINDE10.1. Introduction 22710.2. Fundamentals of wind turbine systems and failure modes 22810.3. Predictive maintenance (PdM) techniques for wind turbines 23010.4. AI for predictive maintenance and fault diagnosis 23410.5. Challenges and future directions 23810.6. Conclusion 24010.7. References 241Chapter 11. Integrating AI and IoT Technologies in Smart Wind Farms: Leveraging Predictive Maintenance and Performance Optimization. 245Sathish Kumar D., Vanitha U., Saravanan G. and Ramya G.11.1. Introduction to smart wind farms 24511.2. Challenges in traditional wind farm operations 24811.3. Role of smart technologies in the energy sector 25011.4. Overview of smart wind farms 25111.5. Components of smart wind farms 25111.6. Internet of Things (IoT) in wind farms 25311.7. Cloud-centric IoT architecture 25411.8. Edge-centric IoT architecture 25511.9. Hybrid IoT architecture 25711.10. Sensor deployment for real-time monitoring in wind farms 25811.11. Data acquisition and wireless communication in wind farms 26011.12. Cloud and edge computing for data processing in smart farms 26111.13. AI applications in smart wind farms 26211.14. Performance prediction and optimization algorithms 26411.15. Case studies and real-world implementations 26611.16. Conclusion 26911.17. References 269Chapter 12. AI and IoT for Smart Wind Farms 271Mantena SIREESHA and Mantena Siva Pavan Kumar RAJU12.1. Introduction 27112.2. Fundamentals of AI and IoT in wind farm operations 27312.3. IoT for data acquisition and real-time monitoring 27512.4. AI for predictive maintenance and fault detection 27712.5. Performance optimization and energy production forecasting 28012.6. Challenges and barriers to implementation 28212.7. Future trends and innovations in smart wind farms 28412.8. Conclusion 28512.9. References 287xiv AI and Wind Power 1Chapter 13. Intelligent Wind Power Forecasting and Demand-Responsive Power Scheduling Using LSTMs 293Pranav Raja K.R., Ritik and Karthika S.K.13.1. Introduction 29313.2. Literature review 29513.3. Methodology 30013.4. Results and analysis 31013.5. Conclusion and future work 31213.6. References 313List of Authors 317Index 323