{"product_id":"ai-and-wind-power-1-a-multifaceted-approach-to-sustainable-energy-hardback-9781836690610","title":"AI and Wind Power 1; A Multifaceted Approach to Sustainable Energy (Hardback) 9781836690610","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAI and Wind Power 1\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Multifaceted Approach to Sustainable Energy\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAbhishek Kumar (Edited by), A Kumar (Author), Ananth Kumar T. (Edited by), Ashutosh Kumar Dubey (Edited by), Arun Lal Srivastav (Edited by), J. Reyes Juarez-Ramirez (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781836690610, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 12 June 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e23.5 x 15.6 x 2.3 cm, 0.659 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIn the critical global transition to sustainable energy, the integration of artificial intelligence (AI) with wind power stands as a pivotal technological frontier.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAI and Wind Power 1\u003c\/i\u003e provides a comprehensive, practical guide to this transformative synergy. This volume delves deep into the core applications of AI, from leveraging deep learning for precise wind resource assessment and sophisticated farm design to deploying advanced algorithms for predictive maintenance and fault diagnosis. The book presents detailed examinations of cutting-edge frameworks, including digital twins, IoT-enabled smart farms and adaptive AI controllers, all aimed at maximizing energy yield, reducing operational costs and enhancing system reliability.\u003c\/p\u003e \u003cp\u003eBy combining rigorous technical analysis with real-world case studies, this book equips engineers, data scientists and energy professionals with the knowledge to implement intelligent solutions, which make wind energy more efficient, resilient and integral to a smarter grid. It is an indispensable resource for anyone dedicated to advancing the technical frontier of renewable energy.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003cbr\u003e\u003ci\u003eAbhishek KUMAR, Ananth Kumar T., Ashutosh Kumar DUBEY, Arun Lal SRIVASTAV and J. Reyes JUÁREZ-RAMÍREZ\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Harnessing the Power of the Wind: A Detailed Exploration of Wind Energy Fundamentals and the Pivotal Role of Emerging AI Techniques 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. VENKATESH and D. VETRITHANGAM\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction to wind energy 2\u003cbr\u003e1.2. Fundamentals of wind energy generation 6\u003cbr\u003e1.3. Wind energy systems and grid integration 12\u003cbr\u003e1.4. Emerging AI techniques in wind energy 14\u003cbr\u003e1.5. Predictive maintenance and performance monitoring 17\u003cbr\u003e1.6. AI in wind farm design and layout optimization 21\u003cbr\u003e1.7. Energy forecasting and demand response with AI 24\u003cbr\u003e1.8. Future trends and innovations in wind energy and AI 26\u003cbr\u003e1.9. Conclusion 30\u003cbr\u003e1.10. References 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. A Terrain-Fused Spatio-Temporal Deep Learning Framework for Accurate Wind Resource Assessment and Forecasting Using TFS-TWF 35\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuresh Kumar K., Kirubha Sagar T., Arun Prakash P. and Praveen Kumar M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 36\u003cbr\u003e2.2. Literature survey 38\u003cbr\u003e2.3. Proposed work 42\u003cbr\u003e2.4. TFS-TWF proposed architecture 43\u003cbr\u003e2.5. Implementation and methodology 45\u003cbr\u003e2.6. Results and discussion 47\u003cbr\u003e2.7. Performance in Region C's hilly terrain 49\u003cbr\u003e2.8. Projected horizon performance 49\u003cbr\u003e2.9. Study of ablation 50\u003cbr\u003e2.10. Estimating uncertainty and dependability 51\u003cbr\u003e2.11. Summary of comparative performance 52\u003cbr\u003e2.12. Prospects and remarks 52\u003cbr\u003e2.13. Conclusion 53\u003cbr\u003e2.14. References 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Deep Fuzzy-Optimized CLSTM–BERT Algorithm with Adaptive Learning for Efficient Wind Farm Design and Energy Management 57\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrabbu Sankar P., Yaashuwanth C., Prathibanandhi K. and S. RAMESH\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 58\u003cbr\u003e3.2. Literature review 61\u003cbr\u003e3.3. Proposed methodology 66\u003cbr\u003e3.4. Results and discussion 73\u003cbr\u003e3.5. Conclusion 79\u003cbr\u003e3.6. References 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Optimizing Wind Energy with AI 83\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHarshvardhan KUNWAR, Shruti ROY, Ahanya BANERJI and Sayantika MUKHERJEE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 83\u003cbr\u003e4.2. Literature review 85\u003cbr\u003e4.3. Methodology 88\u003cbr\u003e4.5. Case studies and real-world applications 94\u003cbr\u003e4.6. AI versus traditional methods: what is the difference? 97\u003cbr\u003e4.7. Challenges and limitations of using AI in wind energy 98\u003cbr\u003e4.8. Future outlook and recommendations 100\u003cbr\u003e4.9. Scalability in developing countries 102\u003cbr\u003e4.10. Conclusion 104\u003cbr\u003e4.11. References 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Integration of Artificial Intelligence and Wind Power: An Orientation Technique. 109\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJohncy Bai J., T.S. SIVARANI, S. JAISIVA, Srividhya J.P. and Gayathri A.R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 109\u003cbr\u003e5.2. Introduction to wind power 110\u003cbr\u003e5.3. Overview of AI in renewable energy 123\u003cbr\u003e5.4. AI applications in wind power 124\u003cbr\u003e5.5. Popular AI techniques in wind power 127\u003cbr\u003e5.6. Challenges of AI in wind power 128\u003cbr\u003e5.7. Future trends and directions for research 129\u003cbr\u003e5.8. Conclusion 135\u003cbr\u003e5.9. References 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Predictive Maintenance and Fault Diagnosis of Wind Turbines Using AI 139\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKarthick Manoj R. and Aasha Nandhini S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 139\u003cbr\u003e6.2. Related work 142\u003cbr\u003e6.3. Methodology 145\u003cbr\u003e6.4. Results and discussion 150\u003cbr\u003e6.5. Conclusion and future work 155\u003cbr\u003e6.6. References 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. A Comprehensive Review of Digital Twin-Enabled AI Models for Interpretable and Scalable Fault Diagnosis in Wind Turbines. 159\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eN.C. DESAI and Priyanka P. SHINDE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 159\u003cbr\u003e7.2. Overview of wind turbine fault diagnosis 160\u003cbr\u003e7.3. Digital twin technology: concept and role in wind turbines 162\u003cbr\u003e7.4. AI techniques in fault diagnosis 164\u003cbr\u003e7.5. Integration of digital twin and AI: synergies and architectures 166\u003cbr\u003e7.6. Explainable AI for interpretability 169\u003cbr\u003e7.7. Sensor fusion and multimodal data integration 170\u003cbr\u003e7.8. Uncertainty quantification in AI models 172\u003cbr\u003e7.9. Validation strategies and simulation frameworks 175\u003cbr\u003e7.10. Scalability considerations for large wind farms 177\u003cbr\u003e7.11. Open challenges and future directions 179\u003cbr\u003e7.12. Conclusion 181\u003cbr\u003e7.13. References 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. A Hybrid AI-Driven Digital Twin Technology for Predictive Maintenance and Fault Diagnosis in Wind Turbines under Variable Environmental Conditions using EcoHyTwin Framework 187\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMahesh Kumar S., Suresh Kumar K., Arun Prakash P. and Karthick Raja M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 188\u003cbr\u003e8.2. Literature survey 191\u003cbr\u003e8.3. Proposed work 195\u003cbr\u003e8.4. Algorithm, implementation and expected outcomes 198\u003cbr\u003e8.5. Dataset and experimental setup 199\u003cbr\u003e8.6. Fault detection accuracy 199\u003cbr\u003e8.7. RUL prediction 200\u003cbr\u003e8.8. Environmental adaptability performance 201\u003cbr\u003e8.9. Fault-type classification performance 201\u003cbr\u003e8.10. Energy efficiency and maintenance cost reduction 202\u003cbr\u003e8.11. Hybrid AI learning efficiency 202\u003cbr\u003e8.12. Comparative analysis with existing approaches 203\u003cbr\u003e8.13. Discussion 204\u003cbr\u003e8.14. Conclusion 204\u003cbr\u003e8.15. References 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. An AI-Driven Framework for Predictive Maintenance and Adaptive Control in Hybrid Wind Farms Using BreezeSenseAI Algorithm 209\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuresh Kumar K., Karthikesh N., Anandaraj A. and Yuvaraj S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 210\u003cbr\u003e9.2. Overview of the BreezeSenseAI framework 216\u003cbr\u003e9.3. Methodology and system architecture 217\u003cbr\u003e9.4. Implementation, evaluation and novel contributions 218\u003cbr\u003e9.5. Adaptive control optimization 221\u003cbr\u003e9.6. Turbine health index (THI) evaluation 222\u003cbr\u003e9.7. Energy efficiency and cost reduction 222\u003cbr\u003e9.8. Real-time adaptability evaluation 223\u003cbr\u003e9.9. Comparative analysis with existing models 223\u003cbr\u003e9.10. Conclusion 224\u003cbr\u003e9.11. References 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. AI-Powered Predictive Maintenance for Wind Energy. 227\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAnurag WAZARKAR, Pratik GUNJALKAR, Tanmay SAWANT, Sanket BABAR, Bhushan S. YELURE and Priyanka P. SHINDE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 227\u003cbr\u003e10.2. Fundamentals of wind turbine systems and failure modes 228\u003cbr\u003e10.3. Predictive maintenance (PdM) techniques for wind turbines 230\u003cbr\u003e10.4. AI for predictive maintenance and fault diagnosis 234\u003cbr\u003e10.5. Challenges and future directions 238\u003cbr\u003e10.6. Conclusion 240\u003cbr\u003e10.7. References 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. Integrating AI and IoT Technologies in Smart Wind Farms: Leveraging Predictive Maintenance and Performance Optimization. 245\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSathish Kumar D., Vanitha U., Saravanan G. and Ramya G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction to smart wind farms 245\u003cbr\u003e11.2. Challenges in traditional wind farm operations 248\u003cbr\u003e11.3. Role of smart technologies in the energy sector 250\u003cbr\u003e11.4. Overview of smart wind farms 251\u003cbr\u003e11.5. Components of smart wind farms 251\u003cbr\u003e11.6. Internet of Things (IoT) in wind farms 253\u003cbr\u003e11.7. Cloud-centric IoT architecture 254\u003cbr\u003e11.8. Edge-centric IoT architecture 255\u003cbr\u003e11.9. Hybrid IoT architecture 257\u003cbr\u003e11.10. Sensor deployment for real-time monitoring in wind farms 258\u003cbr\u003e11.11. Data acquisition and wireless communication in wind farms 260\u003cbr\u003e11.12. Cloud and edge computing for data processing in smart farms 261\u003cbr\u003e11.13. AI applications in smart wind farms 262\u003cbr\u003e11.14. Performance prediction and optimization algorithms 264\u003cbr\u003e11.15. Case studies and real-world implementations 266\u003cbr\u003e11.16. Conclusion 269\u003cbr\u003e11.17. References 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12. AI and IoT for Smart Wind Farms 271\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMantena SIREESHA and Mantena Siva Pavan Kumar RAJU\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1. Introduction 271\u003cbr\u003e12.2. Fundamentals of AI and IoT in wind farm operations 273\u003cbr\u003e12.3. IoT for data acquisition and real-time monitoring 275\u003cbr\u003e12.4. AI for predictive maintenance and fault detection 277\u003cbr\u003e12.5. Performance optimization and energy production forecasting 280\u003cbr\u003e12.6. Challenges and barriers to implementation 282\u003cbr\u003e12.7. Future trends and innovations in smart wind farms 284\u003cbr\u003e12.8. Conclusion 285\u003cbr\u003e12.9. References 287xiv AI and Wind Power 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13. Intelligent Wind Power Forecasting and Demand-Responsive Power Scheduling Using LSTMs 293\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePranav Raja K.R., Ritik and Karthika S.K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1. Introduction 293\u003cbr\u003e13.2. Literature review 295\u003cbr\u003e13.3. Methodology 300\u003cbr\u003e13.4. Results and analysis 310\u003cbr\u003e13.5. Conclusion and future work 312\u003cbr\u003e13.6. References 313\u003c\/p\u003e \u003cp\u003eList of Authors 317\u003cbr\u003eIndex 323\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446832525592,"sku":"9781836690610","price":123.78,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781836690610.jpg?v=1785115190","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/ai-and-wind-power-1-a-multifaceted-approach-to-sustainable-energy-hardback-9781836690610","provider":"Freshly Printed Books","version":"1.0","type":"link"}