{"product_id":"optimization-in-sustainable-energy-methods-and-applications-hardback-9781394242108","title":"Optimization in Sustainable Energy; Methods and Applications (Hardback) 9781394242108","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOptimization in Sustainable Energy\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eMethods and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePrasenjit Chatterjee (Edited by), Kumar Aggarwal (Author), Anita Khosla (Edited by), Ashwani Kumar Aggarwal (Edited by), Gulay Demir (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394242108, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 June 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e528 pages\u003cbr\u003e28 x 19 x 2.6 cm, 0.85 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\u003e\u003cb\u003eThis state-of-the-art book offers cutting-edge optimization techniques and practical decision-making frameworks essential for enhancing the efficiency and reliability of sustainable energy systems, making it an invaluable resource for researchers, policymakers, and energy professionals.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eOptimization in Sustainable Energy: Methods and Applications\u003c\/i\u003e brings together valuable knowledge, methods, and practical examples to help scholars, researchers, professionals, and policymakers address the growing challenges of optimizing sustainable energy. This volume covers a range of topics, including mathematical models, heuristic algorithms, renewable resource management, and energy storage optimization. Each chapter explores a different aspect of sustainable energy, providing both theoretical understanding and practical guidance. \u003c\/p\u003e\n\u003cp\u003eThe volume explores challenges and opportunities surrounding the integration of multi-criteria decision-making techniques in energy planning, highlighting insights on environmental, economic, and social factors influencing the strategic allocation of resources. The use of evolutionary algorithms, machine learning, and metaheuristics to optimize energy storage, distribution, and optimization are also discussed. \u003c\/p\u003e\n\u003cp\u003eThe transition towards sustainable energy is at the forefront of global priorities, driven by the urgent need to mitigate climate change, reduce carbon emissions, and enhance energy security. As countries and industries increasingly prioritize renewable sources like wind, solar, and hydroelectric power, the complexity of optimizing these systems becomes a critical challenge. \u003ci\u003eOptimization in Sustainable Energy: Methods and Applications\u003c\/i\u003e, is a comprehensive exploration of cutting-edge methodologies used to enhance the efficiency, reliability, and performance of sustainable energy systems. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearch scholars, academics, students, policymakers, and industry experts in mechanical engineering, electrical engineering, and energy science.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgment xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Multi-Criteria Optimization and Strategic Planning in Sustainable Energy 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Strategic Roadmap for Turkey’s Sustainable Energy Transition: A Multi-Criteria Perspective 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGülay Demir and Prasenjit Chatterjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.1.1 Research Goals 5\u003c\/p\u003e \u003cp\u003e1.1.1.1 Research Questions 5\u003c\/p\u003e \u003cp\u003e1.1.1.2 Contributions and Novelty 6\u003c\/p\u003e \u003cp\u003e1.1.1.3 Organization of the Chapter 6\u003c\/p\u003e \u003cp\u003e1.2 Literature Review 6\u003c\/p\u003e \u003cp\u003e1.2.1 MCDM Research on Renewable Energy 7\u003c\/p\u003e \u003cp\u003e1.2.2 Studies Used WENSLO and RAWEC Methods 8\u003c\/p\u003e \u003cp\u003e1.2.3 Research Gaps 8\u003c\/p\u003e \u003cp\u003e1.3 Methodology for Research 8\u003c\/p\u003e \u003cp\u003e1.3.1 WENSLO Method for Criteria Prioritization 9\u003c\/p\u003e \u003cp\u003e1.3.2 RAWEC Method to Rank Alternatives 11\u003c\/p\u003e \u003cp\u003e1.3.2.1 Case Study 12\u003c\/p\u003e \u003cp\u003e1.4 Results 14\u003c\/p\u003e \u003cp\u003e1.4.1 Application of WENSLO Method 14\u003c\/p\u003e \u003cp\u003e1.4.2 Application of the RAWEC Method 17\u003c\/p\u003e \u003cp\u003e1.4.3 Sensitivity Analysis 17\u003c\/p\u003e \u003cp\u003e1.4.3.1 Sensitivity Analysis Based on Changes in Criteria Weights 17\u003c\/p\u003e \u003cp\u003e1.4.3.2 Comparison With Other MCDM Methods 20\u003c\/p\u003e \u003cp\u003e1.5 Discussion, Practical and Managerial Implications 21\u003c\/p\u003e \u003cp\u003e1.6 Conclusions, Limitations, and Future Directions 21\u003c\/p\u003e \u003cp\u003eReferences 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Novel p, q-Quasirung Orthopair Fuzzy Group Decision-Making Framework for Selection of Renewable Energy Sources 27\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSanjib Biswas, Gülay Demir and Prasenjit Chatterjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 28\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 30\u003c\/p\u003e \u003cp\u003e2.2.1 Research Gaps 31\u003c\/p\u003e \u003cp\u003e2.2.2 Research Objectives 31\u003c\/p\u003e \u003cp\u003e2.3 Preliminary Concepts: p, q-QOFS 32\u003c\/p\u003e \u003cp\u003e2.4 Fairly Operations and p, q-QOFS Weighted Fairly Aggregation 35\u003c\/p\u003e \u003cp\u003e2.5 Materials and Methods 42\u003c\/p\u003e \u003cp\u003e2.5.1 Theoretical Framework: Selection of Criteria 43\u003c\/p\u003e \u003cp\u003e2.5.2 Expert Group 44\u003c\/p\u003e \u003cp\u003e2.5.3 Methodological Framework 45\u003c\/p\u003e \u003cp\u003e2.5.3.1 Stages in the Methodological Framework 45\u003c\/p\u003e \u003cp\u003e2.5.3.2 Procedural Steps 45\u003c\/p\u003e \u003cp\u003e2.6 Findings 50\u003c\/p\u003e \u003cp\u003e2.7 Discussions 56\u003c\/p\u003e \u003cp\u003e2.8 Conclusion and Future Scope 58\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003eAppendix A 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Evaluating Carbon Footprint Reduction Strategies: A Fuzzy Multi-Criteria Decision-Making Approach 69\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGülay Demir and Prasenjit Chatterjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 70\u003c\/p\u003e \u003cp\u003e3.1.1 Purpose and Importance of the Study 72\u003c\/p\u003e \u003cp\u003e3.1.2 Research Questions 73\u003c\/p\u003e \u003cp\u003e3.1.3 Contributions 74\u003c\/p\u003e \u003cp\u003e3.1.4 Research Gaps 76\u003c\/p\u003e \u003cp\u003e3.2 Literature Review 78\u003c\/p\u003e \u003cp\u003e3.2.1 Carbon Footprint Assessment and MCDM Methods 78\u003c\/p\u003e \u003cp\u003e3.2.2 Studies with WENSLO and RAWEC Methods 80\u003c\/p\u003e \u003cp\u003e3.3 Research Methodology 81\u003c\/p\u003e \u003cp\u003e3.3.1 Fundamentals of FST 81\u003c\/p\u003e \u003cp\u003e3.3.2 F-WENSLO Method for Prioritization of Criteria Affecting Strategies 82\u003c\/p\u003e \u003cp\u003e3.3.3 F-RAWEC Method for Ranking Strategies 85\u003c\/p\u003e \u003cp\u003e3.4 Case Study 87\u003c\/p\u003e \u003cp\u003e3.4.1 Identification and Explanation of Criteria 87\u003c\/p\u003e \u003cp\u003e3.4.2 Carbon Footprint Reduction Strategies 87\u003c\/p\u003e \u003cp\u003e3.4.3 Data Collection and Analysis 87\u003c\/p\u003e \u003cp\u003e3.4.4 Determining Subjective Weights Using F-WENSLO Method 93\u003c\/p\u003e \u003cp\u003e3.4.5 Results of F-RAWEC Application 103\u003c\/p\u003e \u003cp\u003e3.5 Insights, Applications, and Managerial Implications 105\u003c\/p\u003e \u003cp\u003e3.5.1 Analysis of Rankings 105\u003c\/p\u003e \u003cp\u003e3.5.2 Application Implications 106\u003c\/p\u003e \u003cp\u003e3.5.3 Managerial Implications 107\u003c\/p\u003e \u003cp\u003e3.6 Conclusions, Limitations, and Future Directions 108\u003c\/p\u003e \u003cp\u003eReferences 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Prioritizing Sustainable Energy Strategies Using Multi-Criteria Decision-Making Models in Type-2 Neutrosophic Environment 113\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eÖmer Faruk Görçün, Hande Küçükönder and Ahmet Çalık\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 114\u003c\/p\u003e \u003cp\u003e4.2 The Research Background 116\u003c\/p\u003e \u003cp\u003e4.2.1 Common Findings in the Literature 124\u003c\/p\u003e \u003cp\u003e4.2.2 Trends in the Literature 125\u003c\/p\u003e \u003cp\u003e4.2.3 Current State of the Literature 125\u003c\/p\u003e \u003cp\u003e4.2.4 Research and Theoretical Gaps 126\u003c\/p\u003e \u003cp\u003e4.2.5 Motivations and Objectives of the Study 128\u003c\/p\u003e \u003cp\u003e4.3 The Suggested Model 129\u003c\/p\u003e \u003cp\u003e4.3.1 Preliminaries on Neutrosophic Sets 129\u003c\/p\u003e \u003cp\u003e4.3.2 Identifying the Experts’ Reputation 132\u003c\/p\u003e \u003cp\u003e4.3.3 Identifying the Criteria Weights 135\u003c\/p\u003e \u003cp\u003e4.3.3.1 Determining the Subjective Weights of the Criteria 135\u003c\/p\u003e \u003cp\u003e4.3.3.2 Identifying the Objective Weights of the Criteria 136\u003c\/p\u003e \u003cp\u003e4.3.3.3 Associating the Subjective and Objective Weights 139\u003c\/p\u003e \u003cp\u003e4.3.4 Ranking the Alternatives 139\u003c\/p\u003e \u003cp\u003e4.4 Implementing the Model to Identify the Best Sustainable Energy Strategy 142\u003c\/p\u003e \u003cp\u003e4.4.1 The Preparation Process 142\u003c\/p\u003e \u003cp\u003e4.4.1.1 Description of the Problem 142\u003c\/p\u003e \u003cp\u003e4.4.1.2 Forming the Board of Experts 143\u003c\/p\u003e \u003cp\u003e4.4.1.3 Identifying the Criteria and Alternatives 145\u003c\/p\u003e \u003cp\u003e4.4.2 Determining the Weights of the Criteria 153\u003c\/p\u003e \u003cp\u003e4.4.3 Ranking the Alternatives 167\u003c\/p\u003e \u003cp\u003e4.5 Results and Discussions 167\u003c\/p\u003e \u003cp\u003e4.5.1 Rank and Influence of the Criteria 168\u003c\/p\u003e \u003cp\u003e4.5.2 Sustainable Energy Strategies and Their Ranking 168\u003c\/p\u003e \u003cp\u003e4.5.3 Importance, Influence, and Impacts of Results 170\u003c\/p\u003e \u003cp\u003e4.5.4 Novelties, Managerial, and Policy Implications 170\u003c\/p\u003e \u003cp\u003e4.5.5 Theoretical Contributions of the Decision-Making Model 171\u003c\/p\u003e \u003cp\u003e4.6 Conclusions and Future Research Direction 171\u003c\/p\u003e \u003cp\u003eReferences 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 ENTROPY-Based Evaluation of Global Renewable Energy Trends 183\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRahim Arslan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 183\u003c\/p\u003e \u003cp\u003e5.2 Renewable Energy Concepts 185\u003c\/p\u003e \u003cp\u003e5.3 World Countries and Türkiye in Clean Energy 187\u003c\/p\u003e \u003cp\u003e5.4 Evaluation of Renewable Energy Resources Using MCDM Methods 189\u003c\/p\u003e \u003cp\u003e5.5 ENTROPY Method 189\u003c\/p\u003e \u003cp\u003e5.6 Case Study 192\u003c\/p\u003e \u003cp\u003e5.6.1 Renewable Energy Weights According to Installed Capacity 193\u003c\/p\u003e \u003cp\u003e5.7 Conclusions 204\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Optimization Techniques in Sustainable Energy 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Optimization in Sustainable Energy: A Bibliometric Analysis 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee and Divesh Chauhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 210\u003c\/p\u003e \u003cp\u003e6.1.1 Types of Sustainable Energy 211\u003c\/p\u003e \u003cp\u003e6.2 Optimization in Sustainable Energy 212\u003c\/p\u003e \u003cp\u003e6.2.1 Role of Optimization in Sustainable Energy 213\u003c\/p\u003e \u003cp\u003e6.2.2 Bibliometric Analysis 214\u003c\/p\u003e \u003cp\u003e6.2.3 Research Gaps and Research Questions 216\u003c\/p\u003e \u003cp\u003e6.3 Materials and Methods 217\u003c\/p\u003e \u003cp\u003e6.4 The Optimization Results in Sustainable Energy by Bibliometric Analysis 219\u003c\/p\u003e \u003cp\u003e6.4.1 Performance Analysis 219\u003c\/p\u003e \u003cp\u003e6.4.1.1 Overall Review of the Database 219\u003c\/p\u003e \u003cp\u003e6.4.1.2 Annual Publication Increase 220\u003c\/p\u003e \u003cp\u003e6.4.1.3 Average Annual Citations 220\u003c\/p\u003e \u003cp\u003e6.4.1.4 Sankey Diagram 221\u003c\/p\u003e \u003cp\u003e6.4.1.5 Most Cited and Most Published Journals 221\u003c\/p\u003e \u003cp\u003e6.4.1.6 The Affiliations that Matter Most 223\u003c\/p\u003e \u003cp\u003e6.4.1.7 Frequently Cited Authors 223\u003c\/p\u003e \u003cp\u003e6.4.1.8 The Most Productive Countries 224\u003c\/p\u003e \u003cp\u003e6.4.1.9 Most Cited Document 227\u003c\/p\u003e \u003cp\u003e6.4.2 Analysis of Science Mapping 227\u003c\/p\u003e \u003cp\u003e6.4.2.1 Conceptual Structure Map 227\u003c\/p\u003e \u003cp\u003e6.4.2.2 Thematic Map 230\u003c\/p\u003e \u003cp\u003e6.4.2.3 Trend Topics 230\u003c\/p\u003e \u003cp\u003e6.4.2.4 Word Cloud 232\u003c\/p\u003e \u003cp\u003e6.4.2.5 Keyword Co-Occurrence Analysis 232\u003c\/p\u003e \u003cp\u003e6.5 Discussions 233\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 235\u003c\/p\u003e \u003cp\u003eReferences 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 A Novel Optimization-Based Cooling System for Improving Efficacy of Solar Panels Under Changing Climatic Conditions 241\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Sivakumar, A. G. Karthikeyan, R. Karthikeyan and R. Girimurugan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 242\u003c\/p\u003e \u003cp\u003e7.2 Solar PV 242\u003c\/p\u003e \u003cp\u003e7.2.1 Cooling Technologies 245\u003c\/p\u003e \u003cp\u003e7.3 Hybrid PV Panel 247\u003c\/p\u003e \u003cp\u003e7.4 Optimization 248\u003c\/p\u003e \u003cp\u003e7.5 Conventional Optimization Approaches 249\u003c\/p\u003e \u003cp\u003e7.5.1 Genetic Algorithm (GA) 249\u003c\/p\u003e \u003cp\u003e7.5.2 Particle Swarm Optimization (PSO) 250\u003c\/p\u003e \u003cp\u003e7.5.3 Firefly Optimization (FF) 252\u003c\/p\u003e \u003cp\u003e7.5.4 Cuckoo Search (CS) Optimization 252\u003c\/p\u003e \u003cp\u003e7.5.5 Bat Optimization Algorithm 253\u003c\/p\u003e \u003cp\u003e7.5.6 Jelly Fish Optimization 255\u003c\/p\u003e \u003cp\u003e7.5.7 Other Meta-Heuristic Models 257\u003c\/p\u003e \u003cp\u003e7.6 Proposed Optimization Algorithm 258\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 260\u003c\/p\u003e \u003cp\u003eReferences 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Multi-Objective Optimization in Sustainable Energy 267\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSevtap Tırınk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 268\u003c\/p\u003e \u003cp\u003e8.2 Sustainable Development and Energy Sustainability 269\u003c\/p\u003e \u003cp\u003e8.3 Sustainable Energy System Models 271\u003c\/p\u003e \u003cp\u003e8.4 Foundations of Multi-Objective Optimization 276\u003c\/p\u003e \u003cp\u003e8.5 Challenges and Future Directions in Multi-Objective Optimization for Sustainable Energy 281\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 282\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Data Analytics for Performance Optimization in Renewable Energy 291\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAparna Unni and Harpreet Kaur Channi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 292\u003c\/p\u003e \u003cp\u003e9.2 Literature Review 294\u003c\/p\u003e \u003cp\u003e9.2.1 Scope and Objectives 295\u003c\/p\u003e \u003cp\u003e9.3 Renewable Energy Technologies 296\u003c\/p\u003e \u003cp\u003e9.3.1 Challenges in Renewable Energy Performance 297\u003c\/p\u003e \u003cp\u003e9.3.2 Role of Data Analytics in Renewable Energy 297\u003c\/p\u003e \u003cp\u003e9.3.3 Machine Learning Techniques 298\u003c\/p\u003e \u003cp\u003e9.4 Statistical Modeling 300\u003c\/p\u003e \u003cp\u003e9.4.1 Predictive Analytics 301\u003c\/p\u003e \u003cp\u003e9.5 Methodology 302\u003c\/p\u003e \u003cp\u003e9.6 Challenges and Opportunities 305\u003c\/p\u003e \u003cp\u003e9.7 Application Areas of Data Analytics in Renewable Energy 309\u003c\/p\u003e \u003cp\u003e9.8 Real-Time Implementation Using PVsyst 314\u003c\/p\u003e \u003cp\u003e9.9 Top World-Level Case Studies 316\u003c\/p\u003e \u003cp\u003e9.9.1 Wind Farm Optimization in Denmark 316\u003c\/p\u003e \u003cp\u003e9.9.2 Solar Energy Grid Management in Germany 317\u003c\/p\u003e \u003cp\u003e9.9.3 Hydroelectric Power Plant Efficiency in Canada 318\u003c\/p\u003e \u003cp\u003e9.9.4 Energy Storage Optimization in California 318\u003c\/p\u003e \u003cp\u003e9.9.5 Smart Grid Implementation in South Korea 319\u003c\/p\u003e \u003cp\u003e9.9.6 Future Directions 321\u003c\/p\u003e \u003cp\u003e9.10 Conclusion 323\u003c\/p\u003e \u003cp\u003eReferences 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Integration of Smart Grids in Energy Optimization 329\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHarpreet Kaur Channi, Ramandeep Sandhu and Aayush Anand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 330\u003c\/p\u003e \u003cp\u003e10.1.1 Literature Survey 331\u003c\/p\u003e \u003cp\u003e10.1.2 Scope and Significance of the Study 332\u003c\/p\u003e \u003cp\u003e10.2 Smart Grid Fundamentals 333\u003c\/p\u003e \u003cp\u003e10.2.1 Renewable Energy Integration 334\u003c\/p\u003e \u003cp\u003e10.3 Demand-Side Management 337\u003c\/p\u003e \u003cp\u003e10.3.1 Demand-Side Management Techniques 339\u003c\/p\u003e \u003cp\u003e10.4 Data Analytics in Smart Grid 341\u003c\/p\u003e \u003cp\u003e10.4.1 Artificial Intelligence and Machine Learning Applications in Smart Grid 343\u003c\/p\u003e \u003cp\u003e10.4.2 Energy Storage Systems in Smart Grid 345\u003c\/p\u003e \u003cp\u003e10.5 Smart Grid Deployment Worldwide 346\u003c\/p\u003e \u003cp\u003e10.5.1 Clean, Reliable, and Resilient Electricity Systems Need Smart Grids 347\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 352\u003c\/p\u003e \u003cp\u003eReferences 353\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Markov Model-Based Reliability Evaluation of Multiport Converter Fed Induction Motor Drive for Electric Vehicle Applications 357\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManas Taneja and Dheeraj Joshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 357\u003c\/p\u003e \u003cp\u003e11.2 Markov’s Modeling 359\u003c\/p\u003e \u003cp\u003e11.3 Thermal Model 361\u003c\/p\u003e \u003cp\u003e11.4 Transition Rate Evaluation 362\u003c\/p\u003e \u003cp\u003e11.5 Genetic Algorithm 364\u003c\/p\u003e \u003cp\u003e11.6 Reliability Calculations 365\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 369\u003c\/p\u003e \u003cp\u003eReferences 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Forecasting Wind Energy Produced from Wind Turbine: A Markov Chain-Based Approach 373\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYasin Atci and Sibel Atan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 373\u003c\/p\u003e \u003cp\u003e12.2 Literature Review 375\u003c\/p\u003e \u003cp\u003e12.3 Wind Energy 376\u003c\/p\u003e \u003cp\u003e12.3.1 Wind Energy Potential 377\u003c\/p\u003e \u003cp\u003e12.3.2 Wind Theorems 379\u003c\/p\u003e \u003cp\u003e12.3.2.1 Betz Theorem 379\u003c\/p\u003e \u003cp\u003e12.3.2.2 Weibull Distribution 380\u003c\/p\u003e \u003cp\u003e12.3.3 Stochastic Structure of Wind Power 381\u003c\/p\u003e \u003cp\u003e12.4 Markov Processes 383\u003c\/p\u003e \u003cp\u003e12.4.1 Stochastic Processes 383\u003c\/p\u003e \u003cp\u003e12.4.1.1 Index Set 384\u003c\/p\u003e \u003cp\u003e12.4.1.2 State Spaces 384\u003c\/p\u003e \u003cp\u003e12.4.2 Markov Processes 384\u003c\/p\u003e \u003cp\u003e12.4.3 Markov Chains 385\u003c\/p\u003e \u003cp\u003e12.4.3.1 Markov Transition Probabilities Matrix 385\u003c\/p\u003e \u003cp\u003e12.4.3.2 Equilibrium Distributions 386\u003c\/p\u003e \u003cp\u003e12.4.3.3 Multi-Step Transition Probabilities 387\u003c\/p\u003e \u003cp\u003e12.4.3.4 Limit Behavior of Markov Chains 387\u003c\/p\u003e \u003cp\u003e12.5 Wind Energy Forecasting with Markov Chains 388\u003c\/p\u003e \u003cp\u003e12.5.1 Purpose and Content of the Study 389\u003c\/p\u003e \u003cp\u003e12.5.2 Data Set and Data Properties 389\u003c\/p\u003e \u003cp\u003e12.5.2.1 Characteristics of Wind Turbines in Hatay Province 391\u003c\/p\u003e \u003cp\u003e12.5.3 Constructing the Markov Transition Matrix 392\u003c\/p\u003e \u003cp\u003e12.5.4 Cumulative Transition Matrix 395\u003c\/p\u003e \u003cp\u003e12.5.5 Generation of Synthetic Data 396\u003c\/p\u003e \u003cp\u003e12.6 Conclusions and Recommendations 399\u003c\/p\u003e \u003cp\u003eReferences 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Efficient Optimization Techniques for Renewable and Sustainable Energy Systems 405\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSwati Sharma and Ikbal Ali\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 406\u003c\/p\u003e \u003cp\u003e13.2 Renewable Energy Approaches: An Introductory Overview 407\u003c\/p\u003e \u003cp\u003e13.2.1 Renewable Energy Technologies: Types, Applications, and Advancements 410\u003c\/p\u003e \u003cp\u003e13.2.1.1 Solar Energy and Wind Energy 412\u003c\/p\u003e \u003cp\u003e13.2.1.2 Hydro and Ocean Power 417\u003c\/p\u003e \u003cp\u003e13.2.1.3 Geothermal and Bioenergy 418\u003c\/p\u003e \u003cp\u003e13.3 Efficiency Unbound: Unconstrained Optimization Techniques for Renewable Energy Systems 420\u003c\/p\u003e \u003cp\u003e13.3.1 Common Replicas of Unconstrained Optimization Problems 421\u003c\/p\u003e \u003cp\u003e13.3.2 Convex Optimization 422\u003c\/p\u003e \u003cp\u003e13.3.2.1 Duality 423\u003c\/p\u003e \u003cp\u003e13.3.2.2 Simplex Method 425\u003c\/p\u003e \u003cp\u003e13.3.3 Optimization Strategies for Unconstrained Problems 427\u003c\/p\u003e \u003cp\u003e13.3.3.1 Nelder–Mead Method 428\u003c\/p\u003e \u003cp\u003e13.3.3.2 Golden Section Search Method (GSS) 429\u003c\/p\u003e \u003cp\u003e13.3.3.3 Fibonacci Search 430\u003c\/p\u003e \u003cp\u003e13.3.3.4 Hookes’ and Jeeves’ Method 430\u003c\/p\u003e \u003cp\u003e13.3.3.5 Gradient Descent Method 432\u003c\/p\u003e \u003cp\u003e13.3.3.6 Coordinate Descent Method 432\u003c\/p\u003e \u003cp\u003e13.4 Enhancing Renewable Energy Efficiency: Constrained Optimization Methods 433\u003c\/p\u003e \u003cp\u003e13.4.1 Particle Swarm Optimization 433\u003c\/p\u003e \u003cp\u003e13.4.2 Genetic Algorithm 435\u003c\/p\u003e \u003cp\u003e13.4.3 Simulated Annealing 439\u003c\/p\u003e \u003cp\u003e13.4.4 Ant Colony Optimization 441\u003c\/p\u003e \u003cp\u003e13.4.5 Firefly Optimization 442\u003c\/p\u003e \u003cp\u003e13.4.6 Artificial Bee Colony Optimization 444\u003c\/p\u003e \u003cp\u003e13.4.7 Gray Wolf Optimization 446\u003c\/p\u003e \u003cp\u003e13.4.8 Red Fox Optimization 448\u003c\/p\u003e \u003cp\u003e13.4.9 Jaya Algorithm 450\u003c\/p\u003e \u003cp\u003e13.4.10 Teaching–Learning-Based Optimization (TLBO) 451\u003c\/p\u003e \u003cp\u003e13.4.11 Artificial Immune System 452\u003c\/p\u003e \u003cp\u003e13.4.12 Game Theory 453\u003c\/p\u003e \u003cp\u003e13.4.13 Mixed Integer Linear Programming 454\u003c\/p\u003e \u003cp\u003e13.5 Conclusions and Discussion 455\u003c\/p\u003e \u003cp\u003eReferences 456\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Energy Optimization: Challenges, Issues, and Role of Machine Learning Techniques 465\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnshuka Bansal, Ashwani Kumar Aggarwal and Anita Khosla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 466\u003c\/p\u003e \u003cp\u003e14.2 Challenges in Energy Optimization 468\u003c\/p\u003e \u003cp\u003e14.3 Energy Optimization Methods 470\u003c\/p\u003e \u003cp\u003e14.4 Role of Machine Learning Methods 473\u003c\/p\u003e \u003cp\u003e14.5 Machine Learning Models 475\u003c\/p\u003e \u003cp\u003e14.6 Conclusions 478\u003c\/p\u003e \u003cp\u003eReferences 479\u003c\/p\u003e \u003cp\u003eIndex 487\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-Scrivener","offers":[{"title":"Brand 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