Jai Govind Singh, PhD, is an associate professor in the Department of Energy, Environment, and Climate Change at the School of Environment, Resources, and Development, Asian Institute of Technology, Bangkok, Thailand. He has completed 19 sponsored research projects with various international organizations and has published over 110 research papers in reputed journals and conferences. His wide net of research areas includes e-vehicle technologies, smart grid and micro-grid design and operation, power system operation and control, electricity market restructuring and power trading, and energy storage technologies. Rupendra Kumar Pachauri, PhD, is an assistant professor in the Electrical and Electronics Engineering Department at the University of Petroleum and Energy Studies, Dehradun, India. He has published over 130 research papers in internationally reputed journals and conferences, as well as several patents. His primary areas of research include solar energy, fuel cell technology, and smart grid operations. Sasidharan Sreedharan, PhD, is an assistant professor at the College of Applied Sciences, Ministry of Higher Education, Sultanate of Oman. He has completed more than 15 sponsored research projects for various international organizations and published over 80 research papers in reputed journals and conferences. His primary areas of research include high-performance computing, AI and machine learning, optimization and cybersecurity, smart grid operations, electrical supply restructuring, and energy storage.

• Preface xv1 Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition 1Krishna Prakash Natarajan and Jai Govind Singh1.1 Introduction 11.2 Methodology 31.2.1 Variational Mode Decomposition 31.2.2 Long Short-Term Memory 51.2.3 Gated Recurrent Units 81.3 Proposed Methodology for Solar Power Forecasting 91.4 Experimental Results and Discussion 101.4.1 Solar PV Dataset 101.4.2 Experimental Setup and Model Training 121.4.3 Experimental Results 151.5 Conclusion 18References 182 Location Analysis and Environmental Validation for Installation of Hybrid Solar-Wind Energy Generation System in Hilly Areas of Uttarakhand: Study Toward Forecasting 21Paramjeet Singh Paliyal, Shyam Kumar Menon, Surajit Mondal and Vikas Thapa2.1 Introduction 222.1.1 Brief Introduction to the State of Uttarakhand 232.1.2 Solar and Wind Energy Availability in Uttarakhand State 242.1.2.1 The Solar Statistics of the Uttarakhand State 242.1.2.2 The Wind Statistics of an Uttarakhand State 292.2 Observations 312.2.1 Satellite Image of Pithoragarh District 312.2.2 Solar Insolation of the District Pithoragarh Region in Uttarakhand 332.2.3 Wind Statistics of the District Pithoragarh Region in Uttarakhand 332.2.4 Monthly Speed Pattern of the Wind in the Study Area and Its Forecasting 352.3 Imperative of Machine Learning for Present Study 372.4 Conclusion 42References 433 Harnessing Wind Energy: Ontological Frameworks for Optimizing Wind Turbine Lifecycle Management and Performance 49Gaurav Jaglan, Aman Jolly, Vikas Pandey, Shashikant and Priyanka Sharma3.1 Introduction 503.2 Fundamentals of Ontologies 513.3 Wind Turbine Life Cycle Overview 533.3.1 Technological Progress 553.4 Ontologies in Wind Turbine Design and Development 553.5 Different Ontologies Used for Wind Energy and Wind Turbine 573.6 Challenges and Opportunities 613.6.1 Dynamic and Evolving Environments 613.6.2 Semantic Interoperability 613.6.3 Scale and Complexity 613.6.4 Human-Computer Interaction 623.6.5 Real-Time Decision Support 623.6.6 Security and Privacy Problems 623.6.7 Future Research Opportunities 623.7 Conclusion and Future Work 63References 644 Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions 67Varun Pratap Singh and Bharti Sharma4.1 Introduction 684.1.1 Overview of Solar Power Forecasting 684.1.2 Importance and Challenges of Accurate Forecasting 694.2 Fundamentals of Solar Power 694.2.1 Key Factors Influencing Solar Power Output 704.3 Statistical Forecasting Techniques 714.3.1 Time Series Forecasting Methods 714.3.1.1 Autoregressive Models (AR) 714.3.1.2 Moving Average Models (MA) 724.3.1.3 Autoregressive Integrated Moving Average (ARIMA) Models 734.3.1.4 Exponential Smoothing Models 734.3.2 Machine Learning Approaches in Solar Forecasting 744.3.2.1 Neural Networks 744.3.2.2 Support Vector Machines (SVMs) 774.3.3 Ensemble Methods 804.4 Environmental Impacts on Solar Power Generation 834.4.1 Influence of Weather Variabilities 834.4.2 Geographical Impact on Solar Radiation 844.5 Future Directions and Innovations 854.5.1 New Technologies and Methodologies Improving Forecasting Accuracy 854.5.2 Integrating AI and Big Data into Solar Energy Systems 864.6 Conclusion 86References 885 Understanding Forecasting Models for Renewable Energy Generation and Market Operation 95Varun Pratap Singh, Ashwani Kumar, Chandan Swaroop Meena and Nitesh Dutt5.1 Introduction to Renewable Energy Forecasting 965.1.1 Importance of Renewable Energy Forecasting 965.1.2 Overview of Forecasting Models 985.1.3 Challenges in Renewable Energy Forecasting 995.2 Types of Forecasting Models for Renewable Energy 1015.2.1 Physical Models 1015.2.1.1 Mesoscale Models 1015.2.1.2 Microscale Models 1025.2.1.3 Satellite-Based Models 1025.2.2 Statistical Models 1025.2.2.1 Time-Series Forecasting 1025.2.2.2 Regression Models 1025.2.2.3 Machine Learning Algorithms 1035.2.3 Hybrid Models 1055.2.3.1 Ensemble Methods 1055.2.3.2 Integrated Physical-Statistical Models 1065.2.3.3 Multi-Model Fusion 1065.2.4 Specialized Models 1075.2.4.1 Persistence Models 1075.2.4.2 Probabilistic Models 1075.2.4.3 Seasonal and Cyclical Models 1075.3 Forecasting Wind and Solar Energy Generation 1095.3.1 Wind Energy Forecasting Techniques 1095.3.1.1 Wind Speed and Direction Forecasting 1095.3.1.2 Turbine Output Prediction 1105.3.2 Solar Energy Forecasting Techniques 1125.3.2.1 Solar Irradiance Models 1125.3.2.2 Photovoltaic Output Prediction 1125.4 Application of Forecasting in Renewable Energy Market Operations 1145.4.1 Impact on Energy Pricing 1155.4.2 Renewable Energy Trading 1155.4.3 Managing Supply and Demand Balance 1155.4.4 Enhancing Grid Stability and Reliability 1165.4.5 Investment and Financial Planning 1165.4.6 Maintenance Scheduling 1165.4.7 Energy Storage Optimization 1175.4.8 Demand Response Programs 1175.5 Advanced Topics in Renewable Energy Forecasting 1185.5.1 Incorporating Climate Change Projections 1185.5.2 Forecasting for Offshore Renewable Energy Sources 1195.5.3 Role of Big Data and IoT in Forecasting 1195.6 Challenges and Future Directions 1205.6.1 Addressing Variability and Uncertainty 1205.6.2 Integrating Emerging Technologies 1215.6.3 Policy and Regulatory Considerations 1225.7 Future Directions 122References 1226 Machine Learning Techniques for Demand Forecasting in the Electricity Sector 131Firuz Ahamed Nahid, Hussain Mahmud Chowdhury and Mohammad Nayeem Jahangir6.1 Introduction 1326.1.1 Motivation and Contribution 1336.2 Overview of Demand Forecasting 1346.2.1 Classification of Demand Forecasting 1346.2.2 Benefits of Load Forecasting 1366.2.2.1 Efficient Resource Utilization 1366.2.2.2 Operational Efficiency for Energy Producers 1376.2.2.3 Enhanced Grid Operations and Reliability 1376.2.2.4 Consumer Benefits and Renewable Energy Integration 1376.2.2.5 Strategic Planning and Policy Support 1376.2.3 Factors Affecting Electricity Demand Forecasting 1386.2.3.1 Temporal Factors 1386.2.3.2 Meteorological Effects 1386.2.3.3 Economic Indicators 1386.2.3.4 Societal Changes 1396.2.3.5 Regulatory and Policy Influences 1396.2.3.6 Complex Interactions 1396.2.4 Challenges of Demand Forecasting in the Electricity Sector 1396.2.5 Demand Forecasting Model Generation Framework 1406.3 Overview of Machine Learning in Demand Forecasting 1426.3.1 Defining Machine Learning 1426.3.2 Categorizing Machine Learning Methods 1436.3.3 Traditional vs. Machine Learning Approaches 1436.3.4 Machine Learning Techniques in Demand Forecasting 1436.3.5 Summary of the Reviewed Papers 1446.4 Machine Learning–Based Demand Forecasting in Thailand’s Metropolitan Areas: An In-Depth Case Study 1606.4.1 Overview 1606.4.2 Model Design and Validation 1616.4.3 Data Management 1616.4.4 Training Process 1626.4.5 Model Performance Evaluation 1626.4.6 Model Performance Evaluation 1636.4.7 Discussion 1646.5 Conclusion 165References 1667 Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price 173Firuz Ahamed Nahid, Mohammad Nayeem Jahangir, Hussain Mahmud Chowdhury and Khadiza Akter7.1 Introduction 1747.1.1 Power Generation Forecasting 1747.1.2 Demand Forecasting 1757.1.3 Electricity Price Forecasting 1757.2 Understanding Power Generation, Demand, and Price Forecasting 1767.2.1 Challenges and Uncertainties in Forecasting Electric Power, Demand, and Price 1767.2.1.1 Power Generation Forecasting Challenges 1777.2.1.2 Demand Forecasting Uncertainties 1777.2.1.3 Price Forecasting Complexities 1787.2.2 The Advantages of Ongoing Forecasting Evaluation in Power Generation, Demand, and Price Forecasting 1787.3 Significance of Accuracy and Reliability in Forecasting Electric Power, Demand, and Price 1807.3.1 For Energy Producers 1807.3.2 For Consumers 1817.3.3 For Energy Markets 1817.4 Strategic Framework for Enhanced Forecast Evaluation 1817.5 Performance Metrics for Forecasting Accuracy in Generation, Demand, and Price of Electricity 1837.5.1 Criteria for Assessing Accuracy 1847.5.2 Category of Forecasting in in Forecasting Electric Power, Demand, and Price 1887.5.2.1 Statistical Metrics 1887.5.2.2 Variability Estimation Metrics 1897.5.2.3 Ramping Characterization Metrics 1987.6 Comparative Analysis of Forecasting Methods in Energy Sector 2037.7 Future Directions 2097.8 Conclusion 210References 2118 Forecasting Electricity Prices Using NNAR Approach: An Emerging Nation Experience 219Sonal Gupta and Deepankar Chakrabarti8.1 Introduction 2208.2 Literature Review 2238.3 Data and Methodology 2268.4 Data Analysis 2288.5 Conclusion 238References 2399 Machine Learning–Enabled Solar Photovoltaic Energy Forecasting for Modern-Day Grid Integration: A Virtual Power Plant Perspective 243Subhajit Roy, Smriti Jaiswal, Manav Sanghi, Mriganka Dhar, Arif Mohammed, Kothalanka K. Pavan, D. C. Das and Nidul Sinha9.1 Introduction 2449.2 Literature Review 2459.3 Application of Machine Learning to Tackle Climatic Constraints 2489.4 Application of ML in Solar PV–Based Generation 2499.4.1 Importance of Solar PV in Modern Electrification System 2499.4.2 Working of Solar PV 2499.4.3 Factors Affecting PV Power Generation 2509.5 Design of a Predictive ML Model 2549.5.1 Kth-Nearest Neighbor (KNN) Algorithm 2559.5.2 The Random Forest Regressor 2569.6 Data Processing for ML Model 2589.6.1 Dataset Preparation 2589.6.2 The Importance of Data Processing in Machine Learning 2589.6.3 Steps Involved in Data Preprocessing 2599.6.4 Visualizing the Dataset 2619.7 MetaLearner Model 2639.7.1 Dataset Preparation 2639.7.2 The MetaLearner’s Operation 2649.7.3 Holding the Model in Place 2659.8 Result and Discussion 2669.8.1 KNN Model 2669.8.2 Feedforward Neural Network (FNN) Model 2689.8.3 Random Forest Model 2699.8.4 MetaLearner Model 2699.9 Conclusion 271References 27210 Scenario Analysis and Practical Approach of Deep Learning and Machine Learning Techniques in the Renewable Energy Sector 279Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri10.1 Introduction 28010.1.1 Literature Survey 28410.2 Building an Intelligent System for Solar PV Analyzer 29310.3 Popular Machine Learning and Deep Learning Techniques for Solar PV Classifications 29410.3.1 Support Vector Machines 29410.3.2 Random Forest Algorithm 29610.4 Convolutional Neural Network 29710.5 Case Study 29910.6 Conclusion and Future Scope 305Appendix: Pseudocode of Algorithms 306Appendix- A: Support Vector Machine 306Appendix- B: Random Forest 306Appendix-C: Convolutional Neural Network 307References 30711 Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential 311Ajay Mittal11.1 Introduction 31111.2 Interconnections Between Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI) 31211.3 Applications of Artificial Intelligence in Assessing Solar Energy Potential 31311.3.1 Predictive Modeling and Evaluation of Solar Systems 31311.3.2 Selection of Optimal Locations for Solar Installations 31311.3.3 Design and Fabrication of Solar Cells 31311.3.4 Optimizing the Efficiency of Solar Panels 31411.4 Machine Learning Techniques in Solar Energy Conservation and Management 31411.4.1 Artificial Neural Networks (ANNs) 31511.4.2 Genetic Algorithm 31611.4.3 Particle Swarm Optimization (PSO) 31611.4.4 Simulated Annealing (SA) 31711.4.5 Random Forest (RF) 31711.4.6 Hybrid Algorithm 31711.5 Conclusion and Future Perspectives 318References 31812 Revolutionizing Solar PV Forecasting with Machine Learning Techniques 321Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri12.1 Introduction 32212.2 Related Work 32512.3 Smart System for Solar PV Forecasting 32812.4 Prominent Machine Learning Techniques for Forecasting 32812.4.1 Support Vector Regression 32812.4.2 Artificial Neural Network 33212.5 Case Study: Forecasting Power Generation of a Solar PV System 33612.6 Conclusion and Future Scope 342Appendix: Pseudo Code of Suggested Algorithms 342References 34313 Machine Learning–Based Prediction of Electrical Load in the Context of Variable Weather Conditions 347Ashutosh Shukla, Supriya and Rupendra Kumar Pachauri13.1 Introduction 34813.2 Previous Work 34913.3 Significance of Work 35013.4 Methodology 35013.4.1 Input Data 35113.4.2 Data Preprocessing 35413.4.3 Electrical Load Forecasting Algorithms 35513.4.3.1 ANN Model for Electrical Load Forecasting 35513.4.3.2 Random Forest Model for Electrical Load Forecasting 35813.5 Comparative Analysis 36013.6 Conclusion 361References 36114 Recent Advancement in Renewable Energy with Artificial Intelligence and Machine Learning 365Sakshi Chaudhary, Aakansha Simra and Gaurav Pandey14.1 Introduction 36514.2 The Growth and Intersection of AI and mlin the World of Renewable Power 36914.3 Machine Learning–Based Forecasting System for Renewable Energy Production 37114.4 AI and ML Applications for Renewable Energy 37614.4.1 Forecasting the Power of Photovoltaic System 37714.4.2 Forecasting the Power of Wind Energy System 37814.5 Approaches and Limitations in AI Application for Renewable Energy 37914.6 Advances and Prospects in AI for Solar and Wind Power 38014.7 Conclusion 381References 381About the Editors 387Index 389

Jai Govind Singh, PhD, is an associate professor in the Department of Energy, Environment, and Climate Change at the School of Environment, Resources, and Development, Asian Institute of Technology, Bangkok, Thailand. He has completed 19 sponsored research projects with various international organizations and has published over 110 research papers in reputed journals and conferences. His wide net of research areas includes e-vehicle technologies, smart grid and micro-grid design and operation, power system operation and control, electricity market restructuring and power trading, and energy storage technologies. Rupendra Kumar Pachauri, PhD, is an assistant professor in the Electrical and Electronics Engineering Department at the University of Petroleum and Energy Studies, Dehradun, India. He has published over 130 research papers in internationally reputed journals and conferences, as well as several patents. His primary areas of research include solar energy, fuel cell technology, and smart grid operations. Sasidharan Sreedharan, PhD, is an assistant professor at the College of Applied Sciences, Ministry of Higher Education, Sultanate of Oman. He has completed more than 15 sponsored research projects for various international organizations and published over 80 research papers in reputed journals and conferences. His primary areas of research include high-performance computing, AI and machine learning, optimization and cybersecurity, smart grid operations, electrical supply restructuring, and energy storage.

• Preface xv1 Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition 1Krishna Prakash Natarajan and Jai Govind Singh1.1 Introduction 11.2 Methodology 31.2.1 Variational Mode Decomposition 31.2.2 Long Short-Term Memory 51.2.3 Gated Recurrent Units 81.3 Proposed Methodology for Solar Power Forecasting 91.4 Experimental Results and Discussion 101.4.1 Solar PV Dataset 101.4.2 Experimental Setup and Model Training 121.4.3 Experimental Results 151.5 Conclusion 18References 182 Location Analysis and Environmental Validation for Installation of Hybrid Solar-Wind Energy Generation System in Hilly Areas of Uttarakhand: Study Toward Forecasting 21Paramjeet Singh Paliyal, Shyam Kumar Menon, Surajit Mondal and Vikas Thapa2.1 Introduction 222.1.1 Brief Introduction to the State of Uttarakhand 232.1.2 Solar and Wind Energy Availability in Uttarakhand State 242.1.2.1 The Solar Statistics of the Uttarakhand State 242.1.2.2 The Wind Statistics of an Uttarakhand State 292.2 Observations 312.2.1 Satellite Image of Pithoragarh District 312.2.2 Solar Insolation of the District Pithoragarh Region in Uttarakhand 332.2.3 Wind Statistics of the District Pithoragarh Region in Uttarakhand 332.2.4 Monthly Speed Pattern of the Wind in the Study Area and Its Forecasting 352.3 Imperative of Machine Learning for Present Study 372.4 Conclusion 42References 433 Harnessing Wind Energy: Ontological Frameworks for Optimizing Wind Turbine Lifecycle Management and Performance 49Gaurav Jaglan, Aman Jolly, Vikas Pandey, Shashikant and Priyanka Sharma3.1 Introduction 503.2 Fundamentals of Ontologies 513.3 Wind Turbine Life Cycle Overview 533.3.1 Technological Progress 553.4 Ontologies in Wind Turbine Design and Development 553.5 Different Ontologies Used for Wind Energy and Wind Turbine 573.6 Challenges and Opportunities 613.6.1 Dynamic and Evolving Environments 613.6.2 Semantic Interoperability 613.6.3 Scale and Complexity 613.6.4 Human-Computer Interaction 623.6.5 Real-Time Decision Support 623.6.6 Security and Privacy Problems 623.6.7 Future Research Opportunities 623.7 Conclusion and Future Work 63References 644 Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions 67Varun Pratap Singh and Bharti Sharma4.1 Introduction 684.1.1 Overview of Solar Power Forecasting 684.1.2 Importance and Challenges of Accurate Forecasting 694.2 Fundamentals of Solar Power 694.2.1 Key Factors Influencing Solar Power Output 704.3 Statistical Forecasting Techniques 714.3.1 Time Series Forecasting Methods 714.3.1.1 Autoregressive Models (AR) 714.3.1.2 Moving Average Models (MA) 724.3.1.3 Autoregressive Integrated Moving Average (ARIMA) Models 734.3.1.4 Exponential Smoothing Models 734.3.2 Machine Learning Approaches in Solar Forecasting 744.3.2.1 Neural Networks 744.3.2.2 Support Vector Machines (SVMs) 774.3.3 Ensemble Methods 804.4 Environmental Impacts on Solar Power Generation 834.4.1 Influence of Weather Variabilities 834.4.2 Geographical Impact on Solar Radiation 844.5 Future Directions and Innovations 854.5.1 New Technologies and Methodologies Improving Forecasting Accuracy 854.5.2 Integrating AI and Big Data into Solar Energy Systems 864.6 Conclusion 86References 885 Understanding Forecasting Models for Renewable Energy Generation and Market Operation 95Varun Pratap Singh, Ashwani Kumar, Chandan Swaroop Meena and Nitesh Dutt5.1 Introduction to Renewable Energy Forecasting 965.1.1 Importance of Renewable Energy Forecasting 965.1.2 Overview of Forecasting Models 985.1.3 Challenges in Renewable Energy Forecasting 995.2 Types of Forecasting Models for Renewable Energy 1015.2.1 Physical Models 1015.2.1.1 Mesoscale Models 1015.2.1.2 Microscale Models 1025.2.1.3 Satellite-Based Models 1025.2.2 Statistical Models 1025.2.2.1 Time-Series Forecasting 1025.2.2.2 Regression Models 1025.2.2.3 Machine Learning Algorithms 1035.2.3 Hybrid Models 1055.2.3.1 Ensemble Methods 1055.2.3.2 Integrated Physical-Statistical Models 1065.2.3.3 Multi-Model Fusion 1065.2.4 Specialized Models 1075.2.4.1 Persistence Models 1075.2.4.2 Probabilistic Models 1075.2.4.3 Seasonal and Cyclical Models 1075.3 Forecasting Wind and Solar Energy Generation 1095.3.1 Wind Energy Forecasting Techniques 1095.3.1.1 Wind Speed and Direction Forecasting 1095.3.1.2 Turbine Output Prediction 1105.3.2 Solar Energy Forecasting Techniques 1125.3.2.1 Solar Irradiance Models 1125.3.2.2 Photovoltaic Output Prediction 1125.4 Application of Forecasting in Renewable Energy Market Operations 1145.4.1 Impact on Energy Pricing 1155.4.2 Renewable Energy Trading 1155.4.3 Managing Supply and Demand Balance 1155.4.4 Enhancing Grid Stability and Reliability 1165.4.5 Investment and Financial Planning 1165.4.6 Maintenance Scheduling 1165.4.7 Energy Storage Optimization 1175.4.8 Demand Response Programs 1175.5 Advanced Topics in Renewable Energy Forecasting 1185.5.1 Incorporating Climate Change Projections 1185.5.2 Forecasting for Offshore Renewable Energy Sources 1195.5.3 Role of Big Data and IoT in Forecasting 1195.6 Challenges and Future Directions 1205.6.1 Addressing Variability and Uncertainty 1205.6.2 Integrating Emerging Technologies 1215.6.3 Policy and Regulatory Considerations 1225.7 Future Directions 122References 1226 Machine Learning Techniques for Demand Forecasting in the Electricity Sector 131Firuz Ahamed Nahid, Hussain Mahmud Chowdhury and Mohammad Nayeem Jahangir6.1 Introduction 1326.1.1 Motivation and Contribution 1336.2 Overview of Demand Forecasting 1346.2.1 Classification of Demand Forecasting 1346.2.2 Benefits of Load Forecasting 1366.2.2.1 Efficient Resource Utilization 1366.2.2.2 Operational Efficiency for Energy Producers 1376.2.2.3 Enhanced Grid Operations and Reliability 1376.2.2.4 Consumer Benefits and Renewable Energy Integration 1376.2.2.5 Strategic Planning and Policy Support 1376.2.3 Factors Affecting Electricity Demand Forecasting 1386.2.3.1 Temporal Factors 1386.2.3.2 Meteorological Effects 1386.2.3.3 Economic Indicators 1386.2.3.4 Societal Changes 1396.2.3.5 Regulatory and Policy Influences 1396.2.3.6 Complex Interactions 1396.2.4 Challenges of Demand Forecasting in the Electricity Sector 1396.2.5 Demand Forecasting Model Generation Framework 1406.3 Overview of Machine Learning in Demand Forecasting 1426.3.1 Defining Machine Learning 1426.3.2 Categorizing Machine Learning Methods 1436.3.3 Traditional vs. Machine Learning Approaches 1436.3.4 Machine Learning Techniques in Demand Forecasting 1436.3.5 Summary of the Reviewed Papers 1446.4 Machine Learning–Based Demand Forecasting in Thailand’s Metropolitan Areas: An In-Depth Case Study 1606.4.1 Overview 1606.4.2 Model Design and Validation 1616.4.3 Data Management 1616.4.4 Training Process 1626.4.5 Model Performance Evaluation 1626.4.6 Model Performance Evaluation 1636.4.7 Discussion 1646.5 Conclusion 165References 1667 Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price 173Firuz Ahamed Nahid, Mohammad Nayeem Jahangir, Hussain Mahmud Chowdhury and Khadiza Akter7.1 Introduction 1747.1.1 Power Generation Forecasting 1747.1.2 Demand Forecasting 1757.1.3 Electricity Price Forecasting 1757.2 Understanding Power Generation, Demand, and Price Forecasting 1767.2.1 Challenges and Uncertainties in Forecasting Electric Power, Demand, and Price 1767.2.1.1 Power Generation Forecasting Challenges 1777.2.1.2 Demand Forecasting Uncertainties 1777.2.1.3 Price Forecasting Complexities 1787.2.2 The Advantages of Ongoing Forecasting Evaluation in Power Generation, Demand, and Price Forecasting 1787.3 Significance of Accuracy and Reliability in Forecasting Electric Power, Demand, and Price 1807.3.1 For Energy Producers 1807.3.2 For Consumers 1817.3.3 For Energy Markets 1817.4 Strategic Framework for Enhanced Forecast Evaluation 1817.5 Performance Metrics for Forecasting Accuracy in Generation, Demand, and Price of Electricity 1837.5.1 Criteria for Assessing Accuracy 1847.5.2 Category of Forecasting in in Forecasting Electric Power, Demand, and Price 1887.5.2.1 Statistical Metrics 1887.5.2.2 Variability Estimation Metrics 1897.5.2.3 Ramping Characterization Metrics 1987.6 Comparative Analysis of Forecasting Methods in Energy Sector 2037.7 Future Directions 2097.8 Conclusion 210References 2118 Forecasting Electricity Prices Using NNAR Approach: An Emerging Nation Experience 219Sonal Gupta and Deepankar Chakrabarti8.1 Introduction 2208.2 Literature Review 2238.3 Data and Methodology 2268.4 Data Analysis 2288.5 Conclusion 238References 2399 Machine Learning–Enabled Solar Photovoltaic Energy Forecasting for Modern-Day Grid Integration: A Virtual Power Plant Perspective 243Subhajit Roy, Smriti Jaiswal, Manav Sanghi, Mriganka Dhar, Arif Mohammed, Kothalanka K. Pavan, D. C. Das and Nidul Sinha9.1 Introduction 2449.2 Literature Review 2459.3 Application of Machine Learning to Tackle Climatic Constraints 2489.4 Application of ML in Solar PV–Based Generation 2499.4.1 Importance of Solar PV in Modern Electrification System 2499.4.2 Working of Solar PV 2499.4.3 Factors Affecting PV Power Generation 2509.5 Design of a Predictive ML Model 2549.5.1 Kth-Nearest Neighbor (KNN) Algorithm 2559.5.2 The Random Forest Regressor 2569.6 Data Processing for ML Model 2589.6.1 Dataset Preparation 2589.6.2 The Importance of Data Processing in Machine Learning 2589.6.3 Steps Involved in Data Preprocessing 2599.6.4 Visualizing the Dataset 2619.7 MetaLearner Model 2639.7.1 Dataset Preparation 2639.7.2 The MetaLearner’s Operation 2649.7.3 Holding the Model in Place 2659.8 Result and Discussion 2669.8.1 KNN Model 2669.8.2 Feedforward Neural Network (FNN) Model 2689.8.3 Random Forest Model 2699.8.4 MetaLearner Model 2699.9 Conclusion 271References 27210 Scenario Analysis and Practical Approach of Deep Learning and Machine Learning Techniques in the Renewable Energy Sector 279Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri10.1 Introduction 28010.1.1 Literature Survey 28410.2 Building an Intelligent System for Solar PV Analyzer 29310.3 Popular Machine Learning and Deep Learning Techniques for Solar PV Classifications 29410.3.1 Support Vector Machines 29410.3.2 Random Forest Algorithm 29610.4 Convolutional Neural Network 29710.5 Case Study 29910.6 Conclusion and Future Scope 305Appendix: Pseudocode of Algorithms 306Appendix- A: Support Vector Machine 306Appendix- B: Random Forest 306Appendix-C: Convolutional Neural Network 307References 30711 Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential 311Ajay Mittal11.1 Introduction 31111.2 Interconnections Between Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI) 31211.3 Applications of Artificial Intelligence in Assessing Solar Energy Potential 31311.3.1 Predictive Modeling and Evaluation of Solar Systems 31311.3.2 Selection of Optimal Locations for Solar Installations 31311.3.3 Design and Fabrication of Solar Cells 31311.3.4 Optimizing the Efficiency of Solar Panels 31411.4 Machine Learning Techniques in Solar Energy Conservation and Management 31411.4.1 Artificial Neural Networks (ANNs) 31511.4.2 Genetic Algorithm 31611.4.3 Particle Swarm Optimization (PSO) 31611.4.4 Simulated Annealing (SA) 31711.4.5 Random Forest (RF) 31711.4.6 Hybrid Algorithm 31711.5 Conclusion and Future Perspectives 318References 31812 Revolutionizing Solar PV Forecasting with Machine Learning Techniques 321Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri12.1 Introduction 32212.2 Related Work 32512.3 Smart System for Solar PV Forecasting 32812.4 Prominent Machine Learning Techniques for Forecasting 32812.4.1 Support Vector Regression 32812.4.2 Artificial Neural Network 33212.5 Case Study: Forecasting Power Generation of a Solar PV System 33612.6 Conclusion and Future Scope 342Appendix: Pseudo Code of Suggested Algorithms 342References 34313 Machine Learning–Based Prediction of Electrical Load in the Context of Variable Weather Conditions 347Ashutosh Shukla, Supriya and Rupendra Kumar Pachauri13.1 Introduction 34813.2 Previous Work 34913.3 Significance of Work 35013.4 Methodology 35013.4.1 Input Data 35113.4.2 Data Preprocessing 35413.4.3 Electrical Load Forecasting Algorithms 35513.4.3.1 ANN Model for Electrical Load Forecasting 35513.4.3.2 Random Forest Model for Electrical Load Forecasting 35813.5 Comparative Analysis 36013.6 Conclusion 361References 36114 Recent Advancement in Renewable Energy with Artificial Intelligence and Machine Learning 365Sakshi Chaudhary, Aakansha Simra and Gaurav Pandey14.1 Introduction 36514.2 The Growth and Intersection of AI and mlin the World of Renewable Power 36914.3 Machine Learning–Based Forecasting System for Renewable Energy Production 37114.4 AI and ML Applications for Renewable Energy 37614.4.1 Forecasting the Power of Photovoltaic System 37714.4.2 Forecasting the Power of Wind Energy System 37814.5 Approaches and Limitations in AI Application for Renewable Energy 37914.6 Advances and Prospects in AI for Solar and Wind Power 38014.7 Conclusion 381References 381About the Editors 387Index 389

Jai Govind Singh, PhD, is an associate professor in the Department of Energy, Environment, and Climate Change at the School of Environment, Resources, and Development, Asian Institute of Technology, Bangkok, Thailand. He has completed 19 sponsored research projects with various international organizations and has published over 110 research papers in reputed journals and conferences. His wide net of research areas includes e-vehicle technologies, smart grid and micro-grid design and operation, power system operation and control, electricity market restructuring and power trading, and energy storage technologies. Rupendra Kumar Pachauri, PhD, is an assistant professor in the Electrical and Electronics Engineering Department at the University of Petroleum and Energy Studies, Dehradun, India. He has published over 130 research papers in internationally reputed journals and conferences, as well as several patents. His primary areas of research include solar energy, fuel cell technology, and smart grid operations. Sasidharan Sreedharan, PhD, is an assistant professor at the College of Applied Sciences, Ministry of Higher Education, Sultanate of Oman. He has completed more than 15 sponsored research projects for various international organizations and published over 80 research papers in reputed journals and conferences. His primary areas of research include high-performance computing, AI and machine learning, optimization and cybersecurity, smart grid operations, electrical supply restructuring, and energy storage.

• Preface xv1 Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition 1Krishna Prakash Natarajan and Jai Govind Singh1.1 Introduction 11.2 Methodology 31.2.1 Variational Mode Decomposition 31.2.2 Long Short-Term Memory 51.2.3 Gated Recurrent Units 81.3 Proposed Methodology for Solar Power Forecasting 91.4 Experimental Results and Discussion 101.4.1 Solar PV Dataset 101.4.2 Experimental Setup and Model Training 121.4.3 Experimental Results 151.5 Conclusion 18References 182 Location Analysis and Environmental Validation for Installation of Hybrid Solar-Wind Energy Generation System in Hilly Areas of Uttarakhand: Study Toward Forecasting 21Paramjeet Singh Paliyal, Shyam Kumar Menon, Surajit Mondal and Vikas Thapa2.1 Introduction 222.1.1 Brief Introduction to the State of Uttarakhand 232.1.2 Solar and Wind Energy Availability in Uttarakhand State 242.1.2.1 The Solar Statistics of the Uttarakhand State 242.1.2.2 The Wind Statistics of an Uttarakhand State 292.2 Observations 312.2.1 Satellite Image of Pithoragarh District 312.2.2 Solar Insolation of the District Pithoragarh Region in Uttarakhand 332.2.3 Wind Statistics of the District Pithoragarh Region in Uttarakhand 332.2.4 Monthly Speed Pattern of the Wind in the Study Area and Its Forecasting 352.3 Imperative of Machine Learning for Present Study 372.4 Conclusion 42References 433 Harnessing Wind Energy: Ontological Frameworks for Optimizing Wind Turbine Lifecycle Management and Performance 49Gaurav Jaglan, Aman Jolly, Vikas Pandey, Shashikant and Priyanka Sharma3.1 Introduction 503.2 Fundamentals of Ontologies 513.3 Wind Turbine Life Cycle Overview 533.3.1 Technological Progress 553.4 Ontologies in Wind Turbine Design and Development 553.5 Different Ontologies Used for Wind Energy and Wind Turbine 573.6 Challenges and Opportunities 613.6.1 Dynamic and Evolving Environments 613.6.2 Semantic Interoperability 613.6.3 Scale and Complexity 613.6.4 Human-Computer Interaction 623.6.5 Real-Time Decision Support 623.6.6 Security and Privacy Problems 623.6.7 Future Research Opportunities 623.7 Conclusion and Future Work 63References 644 Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions 67Varun Pratap Singh and Bharti Sharma4.1 Introduction 684.1.1 Overview of Solar Power Forecasting 684.1.2 Importance and Challenges of Accurate Forecasting 694.2 Fundamentals of Solar Power 694.2.1 Key Factors Influencing Solar Power Output 704.3 Statistical Forecasting Techniques 714.3.1 Time Series Forecasting Methods 714.3.1.1 Autoregressive Models (AR) 714.3.1.2 Moving Average Models (MA) 724.3.1.3 Autoregressive Integrated Moving Average (ARIMA) Models 734.3.1.4 Exponential Smoothing Models 734.3.2 Machine Learning Approaches in Solar Forecasting 744.3.2.1 Neural Networks 744.3.2.2 Support Vector Machines (SVMs) 774.3.3 Ensemble Methods 804.4 Environmental Impacts on Solar Power Generation 834.4.1 Influence of Weather Variabilities 834.4.2 Geographical Impact on Solar Radiation 844.5 Future Directions and Innovations 854.5.1 New Technologies and Methodologies Improving Forecasting Accuracy 854.5.2 Integrating AI and Big Data into Solar Energy Systems 864.6 Conclusion 86References 885 Understanding Forecasting Models for Renewable Energy Generation and Market Operation 95Varun Pratap Singh, Ashwani Kumar, Chandan Swaroop Meena and Nitesh Dutt5.1 Introduction to Renewable Energy Forecasting 965.1.1 Importance of Renewable Energy Forecasting 965.1.2 Overview of Forecasting Models 985.1.3 Challenges in Renewable Energy Forecasting 995.2 Types of Forecasting Models for Renewable Energy 1015.2.1 Physical Models 1015.2.1.1 Mesoscale Models 1015.2.1.2 Microscale Models 1025.2.1.3 Satellite-Based Models 1025.2.2 Statistical Models 1025.2.2.1 Time-Series Forecasting 1025.2.2.2 Regression Models 1025.2.2.3 Machine Learning Algorithms 1035.2.3 Hybrid Models 1055.2.3.1 Ensemble Methods 1055.2.3.2 Integrated Physical-Statistical Models 1065.2.3.3 Multi-Model Fusion 1065.2.4 Specialized Models 1075.2.4.1 Persistence Models 1075.2.4.2 Probabilistic Models 1075.2.4.3 Seasonal and Cyclical Models 1075.3 Forecasting Wind and Solar Energy Generation 1095.3.1 Wind Energy Forecasting Techniques 1095.3.1.1 Wind Speed and Direction Forecasting 1095.3.1.2 Turbine Output Prediction 1105.3.2 Solar Energy Forecasting Techniques 1125.3.2.1 Solar Irradiance Models 1125.3.2.2 Photovoltaic Output Prediction 1125.4 Application of Forecasting in Renewable Energy Market Operations 1145.4.1 Impact on Energy Pricing 1155.4.2 Renewable Energy Trading 1155.4.3 Managing Supply and Demand Balance 1155.4.4 Enhancing Grid Stability and Reliability 1165.4.5 Investment and Financial Planning 1165.4.6 Maintenance Scheduling 1165.4.7 Energy Storage Optimization 1175.4.8 Demand Response Programs 1175.5 Advanced Topics in Renewable Energy Forecasting 1185.5.1 Incorporating Climate Change Projections 1185.5.2 Forecasting for Offshore Renewable Energy Sources 1195.5.3 Role of Big Data and IoT in Forecasting 1195.6 Challenges and Future Directions 1205.6.1 Addressing Variability and Uncertainty 1205.6.2 Integrating Emerging Technologies 1215.6.3 Policy and Regulatory Considerations 1225.7 Future Directions 122References 1226 Machine Learning Techniques for Demand Forecasting in the Electricity Sector 131Firuz Ahamed Nahid, Hussain Mahmud Chowdhury and Mohammad Nayeem Jahangir6.1 Introduction 1326.1.1 Motivation and Contribution 1336.2 Overview of Demand Forecasting 1346.2.1 Classification of Demand Forecasting 1346.2.2 Benefits of Load Forecasting 1366.2.2.1 Efficient Resource Utilization 1366.2.2.2 Operational Efficiency for Energy Producers 1376.2.2.3 Enhanced Grid Operations and Reliability 1376.2.2.4 Consumer Benefits and Renewable Energy Integration 1376.2.2.5 Strategic Planning and Policy Support 1376.2.3 Factors Affecting Electricity Demand Forecasting 1386.2.3.1 Temporal Factors 1386.2.3.2 Meteorological Effects 1386.2.3.3 Economic Indicators 1386.2.3.4 Societal Changes 1396.2.3.5 Regulatory and Policy Influences 1396.2.3.6 Complex Interactions 1396.2.4 Challenges of Demand Forecasting in the Electricity Sector 1396.2.5 Demand Forecasting Model Generation Framework 1406.3 Overview of Machine Learning in Demand Forecasting 1426.3.1 Defining Machine Learning 1426.3.2 Categorizing Machine Learning Methods 1436.3.3 Traditional vs. Machine Learning Approaches 1436.3.4 Machine Learning Techniques in Demand Forecasting 1436.3.5 Summary of the Reviewed Papers 1446.4 Machine Learning–Based Demand Forecasting in Thailand’s Metropolitan Areas: An In-Depth Case Study 1606.4.1 Overview 1606.4.2 Model Design and Validation 1616.4.3 Data Management 1616.4.4 Training Process 1626.4.5 Model Performance Evaluation 1626.4.6 Model Performance Evaluation 1636.4.7 Discussion 1646.5 Conclusion 165References 1667 Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price 173Firuz Ahamed Nahid, Mohammad Nayeem Jahangir, Hussain Mahmud Chowdhury and Khadiza Akter7.1 Introduction 1747.1.1 Power Generation Forecasting 1747.1.2 Demand Forecasting 1757.1.3 Electricity Price Forecasting 1757.2 Understanding Power Generation, Demand, and Price Forecasting 1767.2.1 Challenges and Uncertainties in Forecasting Electric Power, Demand, and Price 1767.2.1.1 Power Generation Forecasting Challenges 1777.2.1.2 Demand Forecasting Uncertainties 1777.2.1.3 Price Forecasting Complexities 1787.2.2 The Advantages of Ongoing Forecasting Evaluation in Power Generation, Demand, and Price Forecasting 1787.3 Significance of Accuracy and Reliability in Forecasting Electric Power, Demand, and Price 1807.3.1 For Energy Producers 1807.3.2 For Consumers 1817.3.3 For Energy Markets 1817.4 Strategic Framework for Enhanced Forecast Evaluation 1817.5 Performance Metrics for Forecasting Accuracy in Generation, Demand, and Price of Electricity 1837.5.1 Criteria for Assessing Accuracy 1847.5.2 Category of Forecasting in in Forecasting Electric Power, Demand, and Price 1887.5.2.1 Statistical Metrics 1887.5.2.2 Variability Estimation Metrics 1897.5.2.3 Ramping Characterization Metrics 1987.6 Comparative Analysis of Forecasting Methods in Energy Sector 2037.7 Future Directions 2097.8 Conclusion 210References 2118 Forecasting Electricity Prices Using NNAR Approach: An Emerging Nation Experience 219Sonal Gupta and Deepankar Chakrabarti8.1 Introduction 2208.2 Literature Review 2238.3 Data and Methodology 2268.4 Data Analysis 2288.5 Conclusion 238References 2399 Machine Learning–Enabled Solar Photovoltaic Energy Forecasting for Modern-Day Grid Integration: A Virtual Power Plant Perspective 243Subhajit Roy, Smriti Jaiswal, Manav Sanghi, Mriganka Dhar, Arif Mohammed, Kothalanka K. Pavan, D. C. Das and Nidul Sinha9.1 Introduction 2449.2 Literature Review 2459.3 Application of Machine Learning to Tackle Climatic Constraints 2489.4 Application of ML in Solar PV–Based Generation 2499.4.1 Importance of Solar PV in Modern Electrification System 2499.4.2 Working of Solar PV 2499.4.3 Factors Affecting PV Power Generation 2509.5 Design of a Predictive ML Model 2549.5.1 Kth-Nearest Neighbor (KNN) Algorithm 2559.5.2 The Random Forest Regressor 2569.6 Data Processing for ML Model 2589.6.1 Dataset Preparation 2589.6.2 The Importance of Data Processing in Machine Learning 2589.6.3 Steps Involved in Data Preprocessing 2599.6.4 Visualizing the Dataset 2619.7 MetaLearner Model 2639.7.1 Dataset Preparation 2639.7.2 The MetaLearner’s Operation 2649.7.3 Holding the Model in Place 2659.8 Result and Discussion 2669.8.1 KNN Model 2669.8.2 Feedforward Neural Network (FNN) Model 2689.8.3 Random Forest Model 2699.8.4 MetaLearner Model 2699.9 Conclusion 271References 27210 Scenario Analysis and Practical Approach of Deep Learning and Machine Learning Techniques in the Renewable Energy Sector 279Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri10.1 Introduction 28010.1.1 Literature Survey 28410.2 Building an Intelligent System for Solar PV Analyzer 29310.3 Popular Machine Learning and Deep Learning Techniques for Solar PV Classifications 29410.3.1 Support Vector Machines 29410.3.2 Random Forest Algorithm 29610.4 Convolutional Neural Network 29710.5 Case Study 29910.6 Conclusion and Future Scope 305Appendix: Pseudocode of Algorithms 306Appendix- A: Support Vector Machine 306Appendix- B: Random Forest 306Appendix-C: Convolutional Neural Network 307References 30711 Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential 311Ajay Mittal11.1 Introduction 31111.2 Interconnections Between Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI) 31211.3 Applications of Artificial Intelligence in Assessing Solar Energy Potential 31311.3.1 Predictive Modeling and Evaluation of Solar Systems 31311.3.2 Selection of Optimal Locations for Solar Installations 31311.3.3 Design and Fabrication of Solar Cells 31311.3.4 Optimizing the Efficiency of Solar Panels 31411.4 Machine Learning Techniques in Solar Energy Conservation and Management 31411.4.1 Artificial Neural Networks (ANNs) 31511.4.2 Genetic Algorithm 31611.4.3 Particle Swarm Optimization (PSO) 31611.4.4 Simulated Annealing (SA) 31711.4.5 Random Forest (RF) 31711.4.6 Hybrid Algorithm 31711.5 Conclusion and Future Perspectives 318References 31812 Revolutionizing Solar PV Forecasting with Machine Learning Techniques 321Supriya, Ashutosh Shukla, Priyanka Sharma and Rupendra Kumar Pachauri12.1 Introduction 32212.2 Related Work 32512.3 Smart System for Solar PV Forecasting 32812.4 Prominent Machine Learning Techniques for Forecasting 32812.4.1 Support Vector Regression 32812.4.2 Artificial Neural Network 33212.5 Case Study: Forecasting Power Generation of a Solar PV System 33612.6 Conclusion and Future Scope 342Appendix: Pseudo Code of Suggested Algorithms 342References 34313 Machine Learning–Based Prediction of Electrical Load in the Context of Variable Weather Conditions 347Ashutosh Shukla, Supriya and Rupendra Kumar Pachauri13.1 Introduction 34813.2 Previous Work 34913.3 Significance of Work 35013.4 Methodology 35013.4.1 Input Data 35113.4.2 Data Preprocessing 35413.4.3 Electrical Load Forecasting Algorithms 35513.4.3.1 ANN Model for Electrical Load Forecasting 35513.4.3.2 Random Forest Model for Electrical Load Forecasting 35813.5 Comparative Analysis 36013.6 Conclusion 361References 36114 Recent Advancement in Renewable Energy with Artificial Intelligence and Machine Learning 365Sakshi Chaudhary, Aakansha Simra and Gaurav Pandey14.1 Introduction 36514.2 The Growth and Intersection of AI and mlin the World of Renewable Power 36914.3 Machine Learning–Based Forecasting System for Renewable Energy Production 37114.4 AI and ML Applications for Renewable Energy 37614.4.1 Forecasting the Power of Photovoltaic System 37714.4.2 Forecasting the Power of Wind Energy System 37814.5 Approaches and Limitations in AI Application for Renewable Energy 37914.6 Advances and Prospects in AI for Solar and Wind Power 38014.7 Conclusion 381References 381About the Editors 387Index 389