{"product_id":"ai-and-wind-power-2-advancing-sustainability-grid-integration-and-future-frameworks-hardback-9781836691426","title":"AI and Wind Power 2; Advancing Sustainability, Grid Integration, and Future Frameworks (Hardback) 9781836691426","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAI and Wind Power 2\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAdvancing Sustainability, Grid Integration, and Future Frameworks\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\"\u003e9781836691426, 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\"\u003e336 pages\u003cbr\u003e23.5 x 15.6 x 2.2 cm, 0.612 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\u003eAs wind power scales from a complementary energy source to a cornerstone of global electricity systems, the challenge is no longer simply generating more clean energy - it is integrating, sustaining and governing the energy within an increasingly complex and interconnected grid.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAI and Wind Power 2\u003c\/i\u003e examines how artificial intelligence (AI) is enabling this critical transition. Moving beyond turbine-level optimization, this book explores AI-driven architectures for hybrid renewable energy systems that unite wind with solar, hydro and storage. It presents advanced frameworks for smart grid management, dynamic balancing of variable resources and real-time sustainability optimization. Dedicated chapters address the economic and market impacts of AI in wind power, its role in shaping policy and regulatory frameworks, emerging applications in offshore wind, generative AI for system design and consumption behavior analysis.\u003c\/p\u003e \u003cp\u003eAn essential resource for engineers, policymakers, researchers and energy professionals, this book illuminates how intelligent systems are forging a more resilient, sustainable and adaptive energy future.\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. AI-Driven Advanced Smart Grid with Optimized Hybrid Renewable Energy Systems 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMary A.G. EZHIL, S. JAISIVA, M. SUTHANTHIRA, R. ANUJA, M. Dhiviya NYCIL and A.S. MONIKANDAN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003cbr\u003e1.2. Overview of renewable energy systems 2\u003cbr\u003e1.3. Evolution phases of AI in hybrid renewable energy systems 8\u003cbr\u003e1.4. Integration of AI in hybrid energy systems 10\u003cbr\u003e1.5. Analyzing the integration of AI models in renewable energy systems 16\u003cbr\u003e1.6. AI-optimized hybrid system design 21\u003cbr\u003e1.7. Performance metrics and evaluation 25\u003cbr\u003e1.8. Challenges and future directions 28\u003cbr\u003e1.9. Conclusion 29\u003cbr\u003e1.10. References 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Implementation of an AI-Driven Hybrid Renewable Energy Management System Using Deep Fuzzy-Based Particle Swarm Optimization (DFB-PSO) 33\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eE. Afreen BANU, Rajasekaran PALANIAPPAN, J.D. Dorathi JAYASEELI and P. ROBERT\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 33\u003cbr\u003e2.2. Review of optimization algorithms in hybrid renewable energy systems 37\u003cbr\u003e2.3. Deep learning, fuzzy logic and swarm intelligence hybrid AI solutions 39\u003cbr\u003e2.4. Architecture and methodology 42\u003cbr\u003e2.5. Implementation process 45\u003cbr\u003e2.6. Results and discussion 48\u003cbr\u003e2.7. Conclusion 53\u003cbr\u003e2.8. References 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Generative AI for Hybrid Renewable Energy Systems (Solar–Wind–Hydro Integration) 57\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMamta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 57\u003cbr\u003e3.2. Literature review 60\u003cbr\u003e3.3. Fundamentals of generative AI in hybrid systems 62\u003cbr\u003e3.4. Proposed framework and methodology 65\u003cbr\u003e3.5. Case study\/experimental analysis 68\u003cbr\u003e3.6. Results and discussion 71\u003cbr\u003e3.7. Challenges and limitations 74\u003cbr\u003e3.8. Future directions 75\u003cbr\u003e3.9. Conclusion 76\u003cbr\u003e3.10. References 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. AI for Enhancing Sustainability in Wind Energy 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKomal MISHRA and Suman CHAHAR\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 81\u003cbr\u003e4.2. Difficulties related to the sustainability of wind energy 83\u003cbr\u003e4.3. Brief explanation of AI techniques 85\u003cbr\u003e4.4. Using AI to find and select wind sites 87\u003cbr\u003e4.5. Using AI in predictive maintenance 88\u003cbr\u003e4.6. Intelligent control systems for wind turbines 90\u003cbr\u003e4.7. Forecasting wind power through the use of machine learning 91\u003cbr\u003e4.8. AI for linking different energy sources and ensuring management 93\u003cbr\u003e4.9. Challenges, limitations and ethical considerations 94\u003cbr\u003e4.10. Future outlook and research directions 95\u003cbr\u003e4.11. Conclusion 98\u003cbr\u003e4.12. References 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Intelligent Energy with AI-Driven Innovations in Wind Power Systems 101\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. RAJASREE, D. LAKSHMI and Malathy BATUMALAY\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 101\u003cbr\u003e5.2. Literature review 105\u003cbr\u003e5.3. Proposed methodology 112\u003cbr\u003e5.4. Results and discussion 117\u003cbr\u003e5.5. Conclusion and future work. 122\u003cbr\u003e5.6. References 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Economic and Market Impacts of AI in Wind Power 127\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMantena Siva Pavan Kumar RAJU and Mantena SIREESHA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 127\u003cbr\u003e6.2. Operational efficiencies and cost savings through AI 130\u003cbr\u003e6.3. Influence of AI on market dynamics 132\u003cbr\u003e6.4. Economic analysis of AI integration in wind projects 134\u003cbr\u003e6.5. Role of AI in wind power financing and investment trends 137\u003cbr\u003e6.6. Limitations and challenges 139\u003cbr\u003e6.7. Future opportunities 141\u003cbr\u003e6.8. Conclusion 143\u003cbr\u003e6.9. References 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. AI for Policy and Regulatory Frameworks in Wind Power 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuman CHAHAR and Komal MISHRA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 151\u003cbr\u003e7.2. Challenges in current policy and regulatory frameworks 152\u003cbr\u003e7.3. Overview of AI technologies relevant to policy and regulation 156\u003cbr\u003e7.4. Applications of AI in wind power policy and regulation 161\u003cbr\u003e7.5. Case studies and global best practices 166\u003cbr\u003e7.6. Future direction 168\u003cbr\u003e7.7. Conclusion 171\u003cbr\u003e7.8. References 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Implementation of an AI-Driven Wind Energy Sustainability Framework Using Reinforcement Learning-Optimized Deep Neuro-Fuzzy Controller (RL-DNFC) 175\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRajasekaran PALANIAPPAN, E. Afreen BANU, P. ROBERT and J.D. Dorathi JAYASEELI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 175\u003cbr\u003e8.2. Literature review 177\u003cbr\u003e8.3. Wind energy control using fuzzy logic 177\u003cbr\u003e8.4. Neural and deep learning models to predict wind power 178\u003cbr\u003e8.5. Adaptive wind energy control with reinforcement learning 178\u003cbr\u003e8.6. Neuro-fuzzy and hybrid reinforcement approaches 179\u003cbr\u003e8.7. System architecture of the AI-driven wind energy sustainability framework 180\u003cbr\u003e8.8. Methodology and algorithmic design 183\u003cbr\u003e8.9. Implementation setup and simulation environment 187\u003cbr\u003e8.10. Experimental results and performance analysis 190\u003cbr\u003e8.11. Discussion 196\u003cbr\u003e8.12. Conclusion 200\u003cbr\u003e8.13. References 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. AI in Offshore Wind Energy Systems 203\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eV. VANITHA and M. YASHICA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Overview of offshore wind energy 204\u003cbr\u003e9.2. AI in offshore wind farms 204\u003cbr\u003e9.3. Case studies 211\u003cbr\u003e9.4. Challenges and future trends 213\u003cbr\u003e9.5. Conclusion 214\u003cbr\u003e9.6. References 215\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. Emerging AI Innovations in Wind Power 217\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. GAYATHRI and V.VANITHA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 218\u003cbr\u003e10.2. Applications of AI in the wind industry 220\u003cbr\u003e10.3. Case studies 231\u003cbr\u003e10.4. Challenges of AI in the wind industry 235\u003cbr\u003e10.5. Conclusion 238\u003cbr\u003e10.6. References 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. Generative AI for Energy Consumption Behavior Analysis 241\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS. VANSHIKA and Neetu RANI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction 242\u003cbr\u003e11.2. Fundamentals of generative AI for energy consumption behavior 243\u003cbr\u003e11.3. Data in energy behavior analysis 247\u003cbr\u003e11.4. Applications of generative AI in energy consumption analysis 249\u003cbr\u003e11.5. Case studies 253\u003cbr\u003e11.6. Challenges and ethical considerations 256\u003cbr\u003e11.7. Conclusion 257\u003cbr\u003e11.8. References 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12. Wind Power Forecasting for Grid Stability Enhancement with Effective Integration of AI Techniques 261\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJ. Johncy BAI, A. Lelin FRED, S. Jaisiva, V. VELMURUGAN and T. Dharma RAJ\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1. Introduction 261\u003cbr\u003e12.2. Overview of wind power prediction 264\u003cbr\u003e12.3. Workflow of wind power prediction 272\u003cbr\u003e12.4. Grid stability 284\u003cbr\u003e12.5. Conclusion 287\u003cbr\u003e12.6. References 288\u003c\/p\u003e \u003cp\u003eList of Authors 291\u003cbr\u003eIndex 295\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":52446833049880,"sku":"9781836691426","price":117.68,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781836691426.jpg?v=1785115196","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/ai-and-wind-power-2-advancing-sustainability-grid-integration-and-future-frameworks-hardback-9781836691426","provider":"Freshly Printed Books","version":"1.0","type":"link"}