Prasenjit Chatterjee, PhD, is a professor and the Dean of Research and Consultancy at the MCKV Institute of Engineering. He has published 135 research papers and 43 books and serves as a lead series editor for several international book series. He is known for his work developing the MARCOS and RAFSI decision-making methods. His research interests include energy optimization, intelligent decision-making, fuzzy computing, sustainability modeling, and supply chain management. Anita Khosla, PhD, is a professor at Manav Rachna International Institute of Research and Studies with over 27 years of teaching experience. She has published three books and over 50 papers in international journals and conferences and served as a speaker and organizer for numerous conferences and seminars. She is known for her coordination in establishing the Factory Automation Lab in conjunction with Mitsubishi Electric India. Ashwani Kumar, PhD, is an associate professor in the Department of Electrical and Instrumentation Engineering at the Sant Longowal Institute of Engineering and Technology, Longowal, India with over 26 years of experience. He has over 70 publications in book chapters and international journals and conferences. He is the recipient of the Monbukagakusho and Quality Improvement Programme scholarships. His research interests include computer vision, artificial intelligence, and remote sensing. Gülay Demir, PhD, is an associate professor at the School of Health Services at Sivas Cumhuriyet University with over 10 years of academic experience. She is the author of three books and 50 scientific articles, and the editor of two books. Her research interests include smart grids, renewable energy, and fuzzy logic.

• Preface xviiAcknowledgment xxiPart I: Multi-Criteria Optimization and Strategic Planning in Sustainable Energy 11 Strategic Roadmap for Turkey’s Sustainable Energy Transition: A Multi-Criteria Perspective 3Gülay Demir and Prasenjit Chatterjee1.1 Introduction 41.1.1 Research Goals 51.1.1.1 Research Questions 51.1.1.2 Contributions and Novelty 61.1.1.3 Organization of the Chapter 61.2 Literature Review 61.2.1 MCDM Research on Renewable Energy 71.2.2 Studies Used WENSLO and RAWEC Methods 81.2.3 Research Gaps 81.3 Methodology for Research 81.3.1 WENSLO Method for Criteria Prioritization 91.3.2 RAWEC Method to Rank Alternatives 111.3.2.1 Case Study 121.4 Results 141.4.1 Application of WENSLO Method 141.4.2 Application of the RAWEC Method 171.4.3 Sensitivity Analysis 171.4.3.1 Sensitivity Analysis Based on Changes in Criteria Weights 171.4.3.2 Comparison With Other MCDM Methods 201.5 Discussion, Practical and Managerial Implications 211.6 Conclusions, Limitations, and Future Directions 21References 232 A Novel p, q-Quasirung Orthopair Fuzzy Group Decision-Making Framework for Selection of Renewable Energy Sources 27Sanjib Biswas, Gülay Demir and Prasenjit Chatterjee2.1 Introduction 282.2 Literature Review 302.2.1 Research Gaps 312.2.2 Research Objectives 312.3 Preliminary Concepts: p, q-QOFS 322.4 Fairly Operations and p, q-QOFS Weighted Fairly Aggregation 352.5 Materials and Methods 422.5.1 Theoretical Framework: Selection of Criteria 432.5.2 Expert Group 442.5.3 Methodological Framework 452.5.3.1 Stages in the Methodological Framework 452.5.3.2 Procedural Steps 452.6 Findings 502.7 Discussions 562.8 Conclusion and Future Scope 58References 59Appendix A 643 Evaluating Carbon Footprint Reduction Strategies: A Fuzzy Multi-Criteria Decision-Making Approach 69Gülay Demir and Prasenjit Chatterjee3.1 Introduction 703.1.1 Purpose and Importance of the Study 723.1.2 Research Questions 733.1.3 Contributions 743.1.4 Research Gaps 763.2 Literature Review 783.2.1 Carbon Footprint Assessment and MCDM Methods 783.2.2 Studies with WENSLO and RAWEC Methods 803.3 Research Methodology 813.3.1 Fundamentals of FST 813.3.2 F-WENSLO Method for Prioritization of Criteria Affecting Strategies 823.3.3 F-RAWEC Method for Ranking Strategies 853.4 Case Study 873.4.1 Identification and Explanation of Criteria 873.4.2 Carbon Footprint Reduction Strategies 873.4.3 Data Collection and Analysis 873.4.4 Determining Subjective Weights Using F-WENSLO Method 933.4.5 Results of F-RAWEC Application 1033.5 Insights, Applications, and Managerial Implications 1053.5.1 Analysis of Rankings 1053.5.2 Application Implications 1063.5.3 Managerial Implications 1073.6 Conclusions, Limitations, and Future Directions 108References 1104 Prioritizing Sustainable Energy Strategies Using Multi-Criteria Decision-Making Models in Type-2 Neutrosophic Environment 113Ömer Faruk Görçün, Hande Küçükönder and Ahmet Çalık4.1 Introduction 1144.2 The Research Background 1164.2.1 Common Findings in the Literature 1244.2.2 Trends in the Literature 1254.2.3 Current State of the Literature 1254.2.4 Research and Theoretical Gaps 1264.2.5 Motivations and Objectives of the Study 1284.3 The Suggested Model 1294.3.1 Preliminaries on Neutrosophic Sets 1294.3.2 Identifying the Experts’ Reputation 1324.3.3 Identifying the Criteria Weights 1354.3.3.1 Determining the Subjective Weights of the Criteria 1354.3.3.2 Identifying the Objective Weights of the Criteria 1364.3.3.3 Associating the Subjective and Objective Weights 1394.3.4 Ranking the Alternatives 1394.4 Implementing the Model to Identify the Best Sustainable Energy Strategy 1424.4.1 The Preparation Process 1424.4.1.1 Description of the Problem 1424.4.1.2 Forming the Board of Experts 1434.4.1.3 Identifying the Criteria and Alternatives 1454.4.2 Determining the Weights of the Criteria 1534.4.3 Ranking the Alternatives 1674.5 Results and Discussions 1674.5.1 Rank and Influence of the Criteria 1684.5.2 Sustainable Energy Strategies and Their Ranking 1684.5.3 Importance, Influence, and Impacts of Results 1704.5.4 Novelties, Managerial, and Policy Implications 1704.5.5 Theoretical Contributions of the Decision-Making Model 1714.6 Conclusions and Future Research Direction 171References 1725 ENTROPY-Based Evaluation of Global Renewable Energy Trends 183Rahim Arslan5.1 Introduction 1835.2 Renewable Energy Concepts 1855.3 World Countries and Türkiye in Clean Energy 1875.4 Evaluation of Renewable Energy Resources Using MCDM Methods 1895.5 ENTROPY Method 1895.6 Case Study 1925.6.1 Renewable Energy Weights According to Installed Capacity 1935.7 Conclusions 204References 205Part II: Optimization Techniques in Sustainable Energy 2076 Optimization in Sustainable Energy: A Bibliometric Analysis 209Rajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee and Divesh Chauhan6.1 Introduction 2106.1.1 Types of Sustainable Energy 2116.2 Optimization in Sustainable Energy 2126.2.1 Role of Optimization in Sustainable Energy 2136.2.2 Bibliometric Analysis 2146.2.3 Research Gaps and Research Questions 2166.3 Materials and Methods 2176.4 The Optimization Results in Sustainable Energy by Bibliometric Analysis 2196.4.1 Performance Analysis 2196.4.1.1 Overall Review of the Database 2196.4.1.2 Annual Publication Increase 2206.4.1.3 Average Annual Citations 2206.4.1.4 Sankey Diagram 2216.4.1.5 Most Cited and Most Published Journals 2216.4.1.6 The Affiliations that Matter Most 2236.4.1.7 Frequently Cited Authors 2236.4.1.8 The Most Productive Countries 2246.4.1.9 Most Cited Document 2276.4.2 Analysis of Science Mapping 2276.4.2.1 Conceptual Structure Map 2276.4.2.2 Thematic Map 2306.4.2.3 Trend Topics 2306.4.2.4 Word Cloud 2326.4.2.5 Keyword Co-Occurrence Analysis 2326.5 Discussions 2336.6 Conclusions 235References 2367 A Novel Optimization-Based Cooling System for Improving Efficacy of Solar Panels Under Changing Climatic Conditions 241J. Sivakumar, A. G. Karthikeyan, R. Karthikeyan and R. Girimurugan7.1 Introduction 2427.2 Solar PV 2427.2.1 Cooling Technologies 2457.3 Hybrid PV Panel 2477.4 Optimization 2487.5 Conventional Optimization Approaches 2497.5.1 Genetic Algorithm (GA) 2497.5.2 Particle Swarm Optimization (PSO) 2507.5.3 Firefly Optimization (FF) 2527.5.4 Cuckoo Search (CS) Optimization 2527.5.5 Bat Optimization Algorithm 2537.5.6 Jelly Fish Optimization 2557.5.7 Other Meta-Heuristic Models 2577.6 Proposed Optimization Algorithm 2587.7 Conclusion 260References 2618 Multi-Objective Optimization in Sustainable Energy 267Sevtap Tırınk8.1 Introduction 2688.2 Sustainable Development and Energy Sustainability 2698.3 Sustainable Energy System Models 2718.4 Foundations of Multi-Objective Optimization 2768.5 Challenges and Future Directions in Multi-Objective Optimization for Sustainable Energy 2818.6 Conclusions 282References 2839 Data Analytics for Performance Optimization in Renewable Energy 291Aparna Unni and Harpreet Kaur Channi9.1 Introduction 2929.2 Literature Review 2949.2.1 Scope and Objectives 2959.3 Renewable Energy Technologies 2969.3.1 Challenges in Renewable Energy Performance 2979.3.2 Role of Data Analytics in Renewable Energy 2979.3.3 Machine Learning Techniques 2989.4 Statistical Modeling 3009.4.1 Predictive Analytics 3019.5 Methodology 3029.6 Challenges and Opportunities 3059.7 Application Areas of Data Analytics in Renewable Energy 3099.8 Real-Time Implementation Using PVsyst 3149.9 Top World-Level Case Studies 3169.9.1 Wind Farm Optimization in Denmark 3169.9.2 Solar Energy Grid Management in Germany 3179.9.3 Hydroelectric Power Plant Efficiency in Canada 3189.9.4 Energy Storage Optimization in California 3189.9.5 Smart Grid Implementation in South Korea 3199.9.6 Future Directions 3219.10 Conclusion 323References 32410 Integration of Smart Grids in Energy Optimization 329Harpreet Kaur Channi, Ramandeep Sandhu and Aayush Anand10.1 Introduction 33010.1.1 Literature Survey 33110.1.2 Scope and Significance of the Study 33210.2 Smart Grid Fundamentals 33310.2.1 Renewable Energy Integration 33410.3 Demand-Side Management 33710.3.1 Demand-Side Management Techniques 33910.4 Data Analytics in Smart Grid 34110.4.1 Artificial Intelligence and Machine Learning Applications in Smart Grid 34310.4.2 Energy Storage Systems in Smart Grid 34510.5 Smart Grid Deployment Worldwide 34610.5.1 Clean, Reliable, and Resilient Electricity Systems Need Smart Grids 34710.6 Conclusion 352References 35311 Markov Model-Based Reliability Evaluation of Multiport Converter Fed Induction Motor Drive for Electric Vehicle Applications 357Manas Taneja and Dheeraj Joshi11.1 Introduction 35711.2 Markov’s Modeling 35911.3 Thermal Model 36111.4 Transition Rate Evaluation 36211.5 Genetic Algorithm 36411.6 Reliability Calculations 36511.7 Conclusion 369References 36912 Forecasting Wind Energy Produced from Wind Turbine: A Markov Chain-Based Approach 373Yasin Atci and Sibel Atan12.1 Introduction 37312.2 Literature Review 37512.3 Wind Energy 37612.3.1 Wind Energy Potential 37712.3.2 Wind Theorems 37912.3.2.1 Betz Theorem 37912.3.2.2 Weibull Distribution 38012.3.3 Stochastic Structure of Wind Power 38112.4 Markov Processes 38312.4.1 Stochastic Processes 38312.4.1.1 Index Set 38412.4.1.2 State Spaces 38412.4.2 Markov Processes 38412.4.3 Markov Chains 38512.4.3.1 Markov Transition Probabilities Matrix 38512.4.3.2 Equilibrium Distributions 38612.4.3.3 Multi-Step Transition Probabilities 38712.4.3.4 Limit Behavior of Markov Chains 38712.5 Wind Energy Forecasting with Markov Chains 38812.5.1 Purpose and Content of the Study 38912.5.2 Data Set and Data Properties 38912.5.2.1 Characteristics of Wind Turbines in Hatay Province 39112.5.3 Constructing the Markov Transition Matrix 39212.5.4 Cumulative Transition Matrix 39512.5.5 Generation of Synthetic Data 39612.6 Conclusions and Recommendations 399References 40213 Efficient Optimization Techniques for Renewable and Sustainable Energy Systems 405Swati Sharma and Ikbal Ali13.1 Introduction 40613.2 Renewable Energy Approaches: An Introductory Overview 40713.2.1 Renewable Energy Technologies: Types, Applications, and Advancements 41013.2.1.1 Solar Energy and Wind Energy 41213.2.1.2 Hydro and Ocean Power 41713.2.1.3 Geothermal and Bioenergy 41813.3 Efficiency Unbound: Unconstrained Optimization Techniques for Renewable Energy Systems 42013.3.1 Common Replicas of Unconstrained Optimization Problems 42113.3.2 Convex Optimization 42213.3.2.1 Duality 42313.3.2.2 Simplex Method 42513.3.3 Optimization Strategies for Unconstrained Problems 42713.3.3.1 Nelder–Mead Method 42813.3.3.2 Golden Section Search Method (GSS) 42913.3.3.3 Fibonacci Search 43013.3.3.4 Hookes’ and Jeeves’ Method 43013.3.3.5 Gradient Descent Method 43213.3.3.6 Coordinate Descent Method 43213.4 Enhancing Renewable Energy Efficiency: Constrained Optimization Methods 43313.4.1 Particle Swarm Optimization 43313.4.2 Genetic Algorithm 43513.4.3 Simulated Annealing 43913.4.4 Ant Colony Optimization 44113.4.5 Firefly Optimization 44213.4.6 Artificial Bee Colony Optimization 44413.4.7 Gray Wolf Optimization 44613.4.8 Red Fox Optimization 44813.4.9 Jaya Algorithm 45013.4.10 Teaching–Learning-Based Optimization (TLBO) 45113.4.11 Artificial Immune System 45213.4.12 Game Theory 45313.4.13 Mixed Integer Linear Programming 45413.5 Conclusions and Discussion 455References 45614 Energy Optimization: Challenges, Issues, and Role of Machine Learning Techniques 465Anshuka Bansal, Ashwani Kumar Aggarwal and Anita Khosla14.1 Introduction 46614.2 Challenges in Energy Optimization 46814.3 Energy Optimization Methods 47014.4 Role of Machine Learning Methods 47314.5 Machine Learning Models 47514.6 Conclusions 478References 479Index 487

Prasenjit Chatterjee, PhD, is a professor and the Dean of Research and Consultancy at the MCKV Institute of Engineering. He has published 135 research papers and 43 books and serves as a lead series editor for several international book series. He is known for his work developing the MARCOS and RAFSI decision-making methods. His research interests include energy optimization, intelligent decision-making, fuzzy computing, sustainability modeling, and supply chain management. Anita Khosla, PhD, is a professor at Manav Rachna International Institute of Research and Studies with over 27 years of teaching experience. She has published three books and over 50 papers in international journals and conferences and served as a speaker and organizer for numerous conferences and seminars. She is known for her coordination in establishing the Factory Automation Lab in conjunction with Mitsubishi Electric India. Ashwani Kumar, PhD, is an associate professor in the Department of Electrical and Instrumentation Engineering at the Sant Longowal Institute of Engineering and Technology, Longowal, India with over 26 years of experience. He has over 70 publications in book chapters and international journals and conferences. He is the recipient of the Monbukagakusho and Quality Improvement Programme scholarships. His research interests include computer vision, artificial intelligence, and remote sensing. Gülay Demir, PhD, is an associate professor at the School of Health Services at Sivas Cumhuriyet University with over 10 years of academic experience. She is the author of three books and 50 scientific articles, and the editor of two books. Her research interests include smart grids, renewable energy, and fuzzy logic.

• Preface xviiAcknowledgment xxiPart I: Multi-Criteria Optimization and Strategic Planning in Sustainable Energy 11 Strategic Roadmap for Turkey’s Sustainable Energy Transition: A Multi-Criteria Perspective 3Gülay Demir and Prasenjit Chatterjee1.1 Introduction 41.1.1 Research Goals 51.1.1.1 Research Questions 51.1.1.2 Contributions and Novelty 61.1.1.3 Organization of the Chapter 61.2 Literature Review 61.2.1 MCDM Research on Renewable Energy 71.2.2 Studies Used WENSLO and RAWEC Methods 81.2.3 Research Gaps 81.3 Methodology for Research 81.3.1 WENSLO Method for Criteria Prioritization 91.3.2 RAWEC Method to Rank Alternatives 111.3.2.1 Case Study 121.4 Results 141.4.1 Application of WENSLO Method 141.4.2 Application of the RAWEC Method 171.4.3 Sensitivity Analysis 171.4.3.1 Sensitivity Analysis Based on Changes in Criteria Weights 171.4.3.2 Comparison With Other MCDM Methods 201.5 Discussion, Practical and Managerial Implications 211.6 Conclusions, Limitations, and Future Directions 21References 232 A Novel p, q-Quasirung Orthopair Fuzzy Group Decision-Making Framework for Selection of Renewable Energy Sources 27Sanjib Biswas, Gülay Demir and Prasenjit Chatterjee2.1 Introduction 282.2 Literature Review 302.2.1 Research Gaps 312.2.2 Research Objectives 312.3 Preliminary Concepts: p, q-QOFS 322.4 Fairly Operations and p, q-QOFS Weighted Fairly Aggregation 352.5 Materials and Methods 422.5.1 Theoretical Framework: Selection of Criteria 432.5.2 Expert Group 442.5.3 Methodological Framework 452.5.3.1 Stages in the Methodological Framework 452.5.3.2 Procedural Steps 452.6 Findings 502.7 Discussions 562.8 Conclusion and Future Scope 58References 59Appendix A 643 Evaluating Carbon Footprint Reduction Strategies: A Fuzzy Multi-Criteria Decision-Making Approach 69Gülay Demir and Prasenjit Chatterjee3.1 Introduction 703.1.1 Purpose and Importance of the Study 723.1.2 Research Questions 733.1.3 Contributions 743.1.4 Research Gaps 763.2 Literature Review 783.2.1 Carbon Footprint Assessment and MCDM Methods 783.2.2 Studies with WENSLO and RAWEC Methods 803.3 Research Methodology 813.3.1 Fundamentals of FST 813.3.2 F-WENSLO Method for Prioritization of Criteria Affecting Strategies 823.3.3 F-RAWEC Method for Ranking Strategies 853.4 Case Study 873.4.1 Identification and Explanation of Criteria 873.4.2 Carbon Footprint Reduction Strategies 873.4.3 Data Collection and Analysis 873.4.4 Determining Subjective Weights Using F-WENSLO Method 933.4.5 Results of F-RAWEC Application 1033.5 Insights, Applications, and Managerial Implications 1053.5.1 Analysis of Rankings 1053.5.2 Application Implications 1063.5.3 Managerial Implications 1073.6 Conclusions, Limitations, and Future Directions 108References 1104 Prioritizing Sustainable Energy Strategies Using Multi-Criteria Decision-Making Models in Type-2 Neutrosophic Environment 113Ömer Faruk Görçün, Hande Küçükönder and Ahmet Çalık4.1 Introduction 1144.2 The Research Background 1164.2.1 Common Findings in the Literature 1244.2.2 Trends in the Literature 1254.2.3 Current State of the Literature 1254.2.4 Research and Theoretical Gaps 1264.2.5 Motivations and Objectives of the Study 1284.3 The Suggested Model 1294.3.1 Preliminaries on Neutrosophic Sets 1294.3.2 Identifying the Experts’ Reputation 1324.3.3 Identifying the Criteria Weights 1354.3.3.1 Determining the Subjective Weights of the Criteria 1354.3.3.2 Identifying the Objective Weights of the Criteria 1364.3.3.3 Associating the Subjective and Objective Weights 1394.3.4 Ranking the Alternatives 1394.4 Implementing the Model to Identify the Best Sustainable Energy Strategy 1424.4.1 The Preparation Process 1424.4.1.1 Description of the Problem 1424.4.1.2 Forming the Board of Experts 1434.4.1.3 Identifying the Criteria and Alternatives 1454.4.2 Determining the Weights of the Criteria 1534.4.3 Ranking the Alternatives 1674.5 Results and Discussions 1674.5.1 Rank and Influence of the Criteria 1684.5.2 Sustainable Energy Strategies and Their Ranking 1684.5.3 Importance, Influence, and Impacts of Results 1704.5.4 Novelties, Managerial, and Policy Implications 1704.5.5 Theoretical Contributions of the Decision-Making Model 1714.6 Conclusions and Future Research Direction 171References 1725 ENTROPY-Based Evaluation of Global Renewable Energy Trends 183Rahim Arslan5.1 Introduction 1835.2 Renewable Energy Concepts 1855.3 World Countries and Türkiye in Clean Energy 1875.4 Evaluation of Renewable Energy Resources Using MCDM Methods 1895.5 ENTROPY Method 1895.6 Case Study 1925.6.1 Renewable Energy Weights According to Installed Capacity 1935.7 Conclusions 204References 205Part II: Optimization Techniques in Sustainable Energy 2076 Optimization in Sustainable Energy: A Bibliometric Analysis 209Rajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee and Divesh Chauhan6.1 Introduction 2106.1.1 Types of Sustainable Energy 2116.2 Optimization in Sustainable Energy 2126.2.1 Role of Optimization in Sustainable Energy 2136.2.2 Bibliometric Analysis 2146.2.3 Research Gaps and Research Questions 2166.3 Materials and Methods 2176.4 The Optimization Results in Sustainable Energy by Bibliometric Analysis 2196.4.1 Performance Analysis 2196.4.1.1 Overall Review of the Database 2196.4.1.2 Annual Publication Increase 2206.4.1.3 Average Annual Citations 2206.4.1.4 Sankey Diagram 2216.4.1.5 Most Cited and Most Published Journals 2216.4.1.6 The Affiliations that Matter Most 2236.4.1.7 Frequently Cited Authors 2236.4.1.8 The Most Productive Countries 2246.4.1.9 Most Cited Document 2276.4.2 Analysis of Science Mapping 2276.4.2.1 Conceptual Structure Map 2276.4.2.2 Thematic Map 2306.4.2.3 Trend Topics 2306.4.2.4 Word Cloud 2326.4.2.5 Keyword Co-Occurrence Analysis 2326.5 Discussions 2336.6 Conclusions 235References 2367 A Novel Optimization-Based Cooling System for Improving Efficacy of Solar Panels Under Changing Climatic Conditions 241J. Sivakumar, A. G. Karthikeyan, R. Karthikeyan and R. Girimurugan7.1 Introduction 2427.2 Solar PV 2427.2.1 Cooling Technologies 2457.3 Hybrid PV Panel 2477.4 Optimization 2487.5 Conventional Optimization Approaches 2497.5.1 Genetic Algorithm (GA) 2497.5.2 Particle Swarm Optimization (PSO) 2507.5.3 Firefly Optimization (FF) 2527.5.4 Cuckoo Search (CS) Optimization 2527.5.5 Bat Optimization Algorithm 2537.5.6 Jelly Fish Optimization 2557.5.7 Other Meta-Heuristic Models 2577.6 Proposed Optimization Algorithm 2587.7 Conclusion 260References 2618 Multi-Objective Optimization in Sustainable Energy 267Sevtap Tırınk8.1 Introduction 2688.2 Sustainable Development and Energy Sustainability 2698.3 Sustainable Energy System Models 2718.4 Foundations of Multi-Objective Optimization 2768.5 Challenges and Future Directions in Multi-Objective Optimization for Sustainable Energy 2818.6 Conclusions 282References 2839 Data Analytics for Performance Optimization in Renewable Energy 291Aparna Unni and Harpreet Kaur Channi9.1 Introduction 2929.2 Literature Review 2949.2.1 Scope and Objectives 2959.3 Renewable Energy Technologies 2969.3.1 Challenges in Renewable Energy Performance 2979.3.2 Role of Data Analytics in Renewable Energy 2979.3.3 Machine Learning Techniques 2989.4 Statistical Modeling 3009.4.1 Predictive Analytics 3019.5 Methodology 3029.6 Challenges and Opportunities 3059.7 Application Areas of Data Analytics in Renewable Energy 3099.8 Real-Time Implementation Using PVsyst 3149.9 Top World-Level Case Studies 3169.9.1 Wind Farm Optimization in Denmark 3169.9.2 Solar Energy Grid Management in Germany 3179.9.3 Hydroelectric Power Plant Efficiency in Canada 3189.9.4 Energy Storage Optimization in California 3189.9.5 Smart Grid Implementation in South Korea 3199.9.6 Future Directions 3219.10 Conclusion 323References 32410 Integration of Smart Grids in Energy Optimization 329Harpreet Kaur Channi, Ramandeep Sandhu and Aayush Anand10.1 Introduction 33010.1.1 Literature Survey 33110.1.2 Scope and Significance of the Study 33210.2 Smart Grid Fundamentals 33310.2.1 Renewable Energy Integration 33410.3 Demand-Side Management 33710.3.1 Demand-Side Management Techniques 33910.4 Data Analytics in Smart Grid 34110.4.1 Artificial Intelligence and Machine Learning Applications in Smart Grid 34310.4.2 Energy Storage Systems in Smart Grid 34510.5 Smart Grid Deployment Worldwide 34610.5.1 Clean, Reliable, and Resilient Electricity Systems Need Smart Grids 34710.6 Conclusion 352References 35311 Markov Model-Based Reliability Evaluation of Multiport Converter Fed Induction Motor Drive for Electric Vehicle Applications 357Manas Taneja and Dheeraj Joshi11.1 Introduction 35711.2 Markov’s Modeling 35911.3 Thermal Model 36111.4 Transition Rate Evaluation 36211.5 Genetic Algorithm 36411.6 Reliability Calculations 36511.7 Conclusion 369References 36912 Forecasting Wind Energy Produced from Wind Turbine: A Markov Chain-Based Approach 373Yasin Atci and Sibel Atan12.1 Introduction 37312.2 Literature Review 37512.3 Wind Energy 37612.3.1 Wind Energy Potential 37712.3.2 Wind Theorems 37912.3.2.1 Betz Theorem 37912.3.2.2 Weibull Distribution 38012.3.3 Stochastic Structure of Wind Power 38112.4 Markov Processes 38312.4.1 Stochastic Processes 38312.4.1.1 Index Set 38412.4.1.2 State Spaces 38412.4.2 Markov Processes 38412.4.3 Markov Chains 38512.4.3.1 Markov Transition Probabilities Matrix 38512.4.3.2 Equilibrium Distributions 38612.4.3.3 Multi-Step Transition Probabilities 38712.4.3.4 Limit Behavior of Markov Chains 38712.5 Wind Energy Forecasting with Markov Chains 38812.5.1 Purpose and Content of the Study 38912.5.2 Data Set and Data Properties 38912.5.2.1 Characteristics of Wind Turbines in Hatay Province 39112.5.3 Constructing the Markov Transition Matrix 39212.5.4 Cumulative Transition Matrix 39512.5.5 Generation of Synthetic Data 39612.6 Conclusions and Recommendations 399References 40213 Efficient Optimization Techniques for Renewable and Sustainable Energy Systems 405Swati Sharma and Ikbal Ali13.1 Introduction 40613.2 Renewable Energy Approaches: An Introductory Overview 40713.2.1 Renewable Energy Technologies: Types, Applications, and Advancements 41013.2.1.1 Solar Energy and Wind Energy 41213.2.1.2 Hydro and Ocean Power 41713.2.1.3 Geothermal and Bioenergy 41813.3 Efficiency Unbound: Unconstrained Optimization Techniques for Renewable Energy Systems 42013.3.1 Common Replicas of Unconstrained Optimization Problems 42113.3.2 Convex Optimization 42213.3.2.1 Duality 42313.3.2.2 Simplex Method 42513.3.3 Optimization Strategies for Unconstrained Problems 42713.3.3.1 Nelder–Mead Method 42813.3.3.2 Golden Section Search Method (GSS) 42913.3.3.3 Fibonacci Search 43013.3.3.4 Hookes’ and Jeeves’ Method 43013.3.3.5 Gradient Descent Method 43213.3.3.6 Coordinate Descent Method 43213.4 Enhancing Renewable Energy Efficiency: Constrained Optimization Methods 43313.4.1 Particle Swarm Optimization 43313.4.2 Genetic Algorithm 43513.4.3 Simulated Annealing 43913.4.4 Ant Colony Optimization 44113.4.5 Firefly Optimization 44213.4.6 Artificial Bee Colony Optimization 44413.4.7 Gray Wolf Optimization 44613.4.8 Red Fox Optimization 44813.4.9 Jaya Algorithm 45013.4.10 Teaching–Learning-Based Optimization (TLBO) 45113.4.11 Artificial Immune System 45213.4.12 Game Theory 45313.4.13 Mixed Integer Linear Programming 45413.5 Conclusions and Discussion 455References 45614 Energy Optimization: Challenges, Issues, and Role of Machine Learning Techniques 465Anshuka Bansal, Ashwani Kumar Aggarwal and Anita Khosla14.1 Introduction 46614.2 Challenges in Energy Optimization 46814.3 Energy Optimization Methods 47014.4 Role of Machine Learning Methods 47314.5 Machine Learning Models 47514.6 Conclusions 478References 479Index 487

Prasenjit Chatterjee, PhD, is a professor and the Dean of Research and Consultancy at the MCKV Institute of Engineering. He has published 135 research papers and 43 books and serves as a lead series editor for several international book series. He is known for his work developing the MARCOS and RAFSI decision-making methods. His research interests include energy optimization, intelligent decision-making, fuzzy computing, sustainability modeling, and supply chain management. Anita Khosla, PhD, is a professor at Manav Rachna International Institute of Research and Studies with over 27 years of teaching experience. She has published three books and over 50 papers in international journals and conferences and served as a speaker and organizer for numerous conferences and seminars. She is known for her coordination in establishing the Factory Automation Lab in conjunction with Mitsubishi Electric India. Ashwani Kumar, PhD, is an associate professor in the Department of Electrical and Instrumentation Engineering at the Sant Longowal Institute of Engineering and Technology, Longowal, India with over 26 years of experience. He has over 70 publications in book chapters and international journals and conferences. He is the recipient of the Monbukagakusho and Quality Improvement Programme scholarships. His research interests include computer vision, artificial intelligence, and remote sensing. Gülay Demir, PhD, is an associate professor at the School of Health Services at Sivas Cumhuriyet University with over 10 years of academic experience. She is the author of three books and 50 scientific articles, and the editor of two books. Her research interests include smart grids, renewable energy, and fuzzy logic.

• Preface xviiAcknowledgment xxiPart I: Multi-Criteria Optimization and Strategic Planning in Sustainable Energy 11 Strategic Roadmap for Turkey’s Sustainable Energy Transition: A Multi-Criteria Perspective 3Gülay Demir and Prasenjit Chatterjee1.1 Introduction 41.1.1 Research Goals 51.1.1.1 Research Questions 51.1.1.2 Contributions and Novelty 61.1.1.3 Organization of the Chapter 61.2 Literature Review 61.2.1 MCDM Research on Renewable Energy 71.2.2 Studies Used WENSLO and RAWEC Methods 81.2.3 Research Gaps 81.3 Methodology for Research 81.3.1 WENSLO Method for Criteria Prioritization 91.3.2 RAWEC Method to Rank Alternatives 111.3.2.1 Case Study 121.4 Results 141.4.1 Application of WENSLO Method 141.4.2 Application of the RAWEC Method 171.4.3 Sensitivity Analysis 171.4.3.1 Sensitivity Analysis Based on Changes in Criteria Weights 171.4.3.2 Comparison With Other MCDM Methods 201.5 Discussion, Practical and Managerial Implications 211.6 Conclusions, Limitations, and Future Directions 21References 232 A Novel p, q-Quasirung Orthopair Fuzzy Group Decision-Making Framework for Selection of Renewable Energy Sources 27Sanjib Biswas, Gülay Demir and Prasenjit Chatterjee2.1 Introduction 282.2 Literature Review 302.2.1 Research Gaps 312.2.2 Research Objectives 312.3 Preliminary Concepts: p, q-QOFS 322.4 Fairly Operations and p, q-QOFS Weighted Fairly Aggregation 352.5 Materials and Methods 422.5.1 Theoretical Framework: Selection of Criteria 432.5.2 Expert Group 442.5.3 Methodological Framework 452.5.3.1 Stages in the Methodological Framework 452.5.3.2 Procedural Steps 452.6 Findings 502.7 Discussions 562.8 Conclusion and Future Scope 58References 59Appendix A 643 Evaluating Carbon Footprint Reduction Strategies: A Fuzzy Multi-Criteria Decision-Making Approach 69Gülay Demir and Prasenjit Chatterjee3.1 Introduction 703.1.1 Purpose and Importance of the Study 723.1.2 Research Questions 733.1.3 Contributions 743.1.4 Research Gaps 763.2 Literature Review 783.2.1 Carbon Footprint Assessment and MCDM Methods 783.2.2 Studies with WENSLO and RAWEC Methods 803.3 Research Methodology 813.3.1 Fundamentals of FST 813.3.2 F-WENSLO Method for Prioritization of Criteria Affecting Strategies 823.3.3 F-RAWEC Method for Ranking Strategies 853.4 Case Study 873.4.1 Identification and Explanation of Criteria 873.4.2 Carbon Footprint Reduction Strategies 873.4.3 Data Collection and Analysis 873.4.4 Determining Subjective Weights Using F-WENSLO Method 933.4.5 Results of F-RAWEC Application 1033.5 Insights, Applications, and Managerial Implications 1053.5.1 Analysis of Rankings 1053.5.2 Application Implications 1063.5.3 Managerial Implications 1073.6 Conclusions, Limitations, and Future Directions 108References 1104 Prioritizing Sustainable Energy Strategies Using Multi-Criteria Decision-Making Models in Type-2 Neutrosophic Environment 113Ömer Faruk Görçün, Hande Küçükönder and Ahmet Çalık4.1 Introduction 1144.2 The Research Background 1164.2.1 Common Findings in the Literature 1244.2.2 Trends in the Literature 1254.2.3 Current State of the Literature 1254.2.4 Research and Theoretical Gaps 1264.2.5 Motivations and Objectives of the Study 1284.3 The Suggested Model 1294.3.1 Preliminaries on Neutrosophic Sets 1294.3.2 Identifying the Experts’ Reputation 1324.3.3 Identifying the Criteria Weights 1354.3.3.1 Determining the Subjective Weights of the Criteria 1354.3.3.2 Identifying the Objective Weights of the Criteria 1364.3.3.3 Associating the Subjective and Objective Weights 1394.3.4 Ranking the Alternatives 1394.4 Implementing the Model to Identify the Best Sustainable Energy Strategy 1424.4.1 The Preparation Process 1424.4.1.1 Description of the Problem 1424.4.1.2 Forming the Board of Experts 1434.4.1.3 Identifying the Criteria and Alternatives 1454.4.2 Determining the Weights of the Criteria 1534.4.3 Ranking the Alternatives 1674.5 Results and Discussions 1674.5.1 Rank and Influence of the Criteria 1684.5.2 Sustainable Energy Strategies and Their Ranking 1684.5.3 Importance, Influence, and Impacts of Results 1704.5.4 Novelties, Managerial, and Policy Implications 1704.5.5 Theoretical Contributions of the Decision-Making Model 1714.6 Conclusions and Future Research Direction 171References 1725 ENTROPY-Based Evaluation of Global Renewable Energy Trends 183Rahim Arslan5.1 Introduction 1835.2 Renewable Energy Concepts 1855.3 World Countries and Türkiye in Clean Energy 1875.4 Evaluation of Renewable Energy Resources Using MCDM Methods 1895.5 ENTROPY Method 1895.6 Case Study 1925.6.1 Renewable Energy Weights According to Installed Capacity 1935.7 Conclusions 204References 205Part II: Optimization Techniques in Sustainable Energy 2076 Optimization in Sustainable Energy: A Bibliometric Analysis 209Rajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee and Divesh Chauhan6.1 Introduction 2106.1.1 Types of Sustainable Energy 2116.2 Optimization in Sustainable Energy 2126.2.1 Role of Optimization in Sustainable Energy 2136.2.2 Bibliometric Analysis 2146.2.3 Research Gaps and Research Questions 2166.3 Materials and Methods 2176.4 The Optimization Results in Sustainable Energy by Bibliometric Analysis 2196.4.1 Performance Analysis 2196.4.1.1 Overall Review of the Database 2196.4.1.2 Annual Publication Increase 2206.4.1.3 Average Annual Citations 2206.4.1.4 Sankey Diagram 2216.4.1.5 Most Cited and Most Published Journals 2216.4.1.6 The Affiliations that Matter Most 2236.4.1.7 Frequently Cited Authors 2236.4.1.8 The Most Productive Countries 2246.4.1.9 Most Cited Document 2276.4.2 Analysis of Science Mapping 2276.4.2.1 Conceptual Structure Map 2276.4.2.2 Thematic Map 2306.4.2.3 Trend Topics 2306.4.2.4 Word Cloud 2326.4.2.5 Keyword Co-Occurrence Analysis 2326.5 Discussions 2336.6 Conclusions 235References 2367 A Novel Optimization-Based Cooling System for Improving Efficacy of Solar Panels Under Changing Climatic Conditions 241J. Sivakumar, A. G. Karthikeyan, R. Karthikeyan and R. Girimurugan7.1 Introduction 2427.2 Solar PV 2427.2.1 Cooling Technologies 2457.3 Hybrid PV Panel 2477.4 Optimization 2487.5 Conventional Optimization Approaches 2497.5.1 Genetic Algorithm (GA) 2497.5.2 Particle Swarm Optimization (PSO) 2507.5.3 Firefly Optimization (FF) 2527.5.4 Cuckoo Search (CS) Optimization 2527.5.5 Bat Optimization Algorithm 2537.5.6 Jelly Fish Optimization 2557.5.7 Other Meta-Heuristic Models 2577.6 Proposed Optimization Algorithm 2587.7 Conclusion 260References 2618 Multi-Objective Optimization in Sustainable Energy 267Sevtap Tırınk8.1 Introduction 2688.2 Sustainable Development and Energy Sustainability 2698.3 Sustainable Energy System Models 2718.4 Foundations of Multi-Objective Optimization 2768.5 Challenges and Future Directions in Multi-Objective Optimization for Sustainable Energy 2818.6 Conclusions 282References 2839 Data Analytics for Performance Optimization in Renewable Energy 291Aparna Unni and Harpreet Kaur Channi9.1 Introduction 2929.2 Literature Review 2949.2.1 Scope and Objectives 2959.3 Renewable Energy Technologies 2969.3.1 Challenges in Renewable Energy Performance 2979.3.2 Role of Data Analytics in Renewable Energy 2979.3.3 Machine Learning Techniques 2989.4 Statistical Modeling 3009.4.1 Predictive Analytics 3019.5 Methodology 3029.6 Challenges and Opportunities 3059.7 Application Areas of Data Analytics in Renewable Energy 3099.8 Real-Time Implementation Using PVsyst 3149.9 Top World-Level Case Studies 3169.9.1 Wind Farm Optimization in Denmark 3169.9.2 Solar Energy Grid Management in Germany 3179.9.3 Hydroelectric Power Plant Efficiency in Canada 3189.9.4 Energy Storage Optimization in California 3189.9.5 Smart Grid Implementation in South Korea 3199.9.6 Future Directions 3219.10 Conclusion 323References 32410 Integration of Smart Grids in Energy Optimization 329Harpreet Kaur Channi, Ramandeep Sandhu and Aayush Anand10.1 Introduction 33010.1.1 Literature Survey 33110.1.2 Scope and Significance of the Study 33210.2 Smart Grid Fundamentals 33310.2.1 Renewable Energy Integration 33410.3 Demand-Side Management 33710.3.1 Demand-Side Management Techniques 33910.4 Data Analytics in Smart Grid 34110.4.1 Artificial Intelligence and Machine Learning Applications in Smart Grid 34310.4.2 Energy Storage Systems in Smart Grid 34510.5 Smart Grid Deployment Worldwide 34610.5.1 Clean, Reliable, and Resilient Electricity Systems Need Smart Grids 34710.6 Conclusion 352References 35311 Markov Model-Based Reliability Evaluation of Multiport Converter Fed Induction Motor Drive for Electric Vehicle Applications 357Manas Taneja and Dheeraj Joshi11.1 Introduction 35711.2 Markov’s Modeling 35911.3 Thermal Model 36111.4 Transition Rate Evaluation 36211.5 Genetic Algorithm 36411.6 Reliability Calculations 36511.7 Conclusion 369References 36912 Forecasting Wind Energy Produced from Wind Turbine: A Markov Chain-Based Approach 373Yasin Atci and Sibel Atan12.1 Introduction 37312.2 Literature Review 37512.3 Wind Energy 37612.3.1 Wind Energy Potential 37712.3.2 Wind Theorems 37912.3.2.1 Betz Theorem 37912.3.2.2 Weibull Distribution 38012.3.3 Stochastic Structure of Wind Power 38112.4 Markov Processes 38312.4.1 Stochastic Processes 38312.4.1.1 Index Set 38412.4.1.2 State Spaces 38412.4.2 Markov Processes 38412.4.3 Markov Chains 38512.4.3.1 Markov Transition Probabilities Matrix 38512.4.3.2 Equilibrium Distributions 38612.4.3.3 Multi-Step Transition Probabilities 38712.4.3.4 Limit Behavior of Markov Chains 38712.5 Wind Energy Forecasting with Markov Chains 38812.5.1 Purpose and Content of the Study 38912.5.2 Data Set and Data Properties 38912.5.2.1 Characteristics of Wind Turbines in Hatay Province 39112.5.3 Constructing the Markov Transition Matrix 39212.5.4 Cumulative Transition Matrix 39512.5.5 Generation of Synthetic Data 39612.6 Conclusions and Recommendations 399References 40213 Efficient Optimization Techniques for Renewable and Sustainable Energy Systems 405Swati Sharma and Ikbal Ali13.1 Introduction 40613.2 Renewable Energy Approaches: An Introductory Overview 40713.2.1 Renewable Energy Technologies: Types, Applications, and Advancements 41013.2.1.1 Solar Energy and Wind Energy 41213.2.1.2 Hydro and Ocean Power 41713.2.1.3 Geothermal and Bioenergy 41813.3 Efficiency Unbound: Unconstrained Optimization Techniques for Renewable Energy Systems 42013.3.1 Common Replicas of Unconstrained Optimization Problems 42113.3.2 Convex Optimization 42213.3.2.1 Duality 42313.3.2.2 Simplex Method 42513.3.3 Optimization Strategies for Unconstrained Problems 42713.3.3.1 Nelder–Mead Method 42813.3.3.2 Golden Section Search Method (GSS) 42913.3.3.3 Fibonacci Search 43013.3.3.4 Hookes’ and Jeeves’ Method 43013.3.3.5 Gradient Descent Method 43213.3.3.6 Coordinate Descent Method 43213.4 Enhancing Renewable Energy Efficiency: Constrained Optimization Methods 43313.4.1 Particle Swarm Optimization 43313.4.2 Genetic Algorithm 43513.4.3 Simulated Annealing 43913.4.4 Ant Colony Optimization 44113.4.5 Firefly Optimization 44213.4.6 Artificial Bee Colony Optimization 44413.4.7 Gray Wolf Optimization 44613.4.8 Red Fox Optimization 44813.4.9 Jaya Algorithm 45013.4.10 Teaching–Learning-Based Optimization (TLBO) 45113.4.11 Artificial Immune System 45213.4.12 Game Theory 45313.4.13 Mixed Integer Linear Programming 45413.5 Conclusions and Discussion 455References 45614 Energy Optimization: Challenges, Issues, and Role of Machine Learning Techniques 465Anshuka Bansal, Ashwani Kumar Aggarwal and Anita Khosla14.1 Introduction 46614.2 Challenges in Energy Optimization 46814.3 Energy Optimization Methods 47014.4 Role of Machine Learning Methods 47314.5 Machine Learning Models 47514.6 Conclusions 478References 479Index 487