Preface xxi
1 Exploring the IoT and AI Technologies in Energy-Efficient Sustainable Agriculture 1
M. Muthumalathi, Ponnarasi Loganathan, P.B. Pankajavalli and A. Priya Dharshini
1.1 Introduction to the Internet of Things and Artificial Intelligence 2
1.2 Essentials of IoT and AI Applications in Agriculture 5
1.2.1 IoT Applications in Agriculture 5
1.2.1.1 Monitor and Sense 5
1.2.1.2 IoT-Enabled Precision Agriculture 5
1.2.1.3 Monitor Livestock 5
1.2.1.4 Managing the Supply Chain 5
1.2.2 AI Applications in Agriculture 6
1.2.2.1 Soil Management 6
1.2.2.2 Weed Management 6
1.3 Robotics in Agriculture 8
1.3.1 Automation of Agricultural Processes 8
1.3.2 Precision Farming 9
1.3.3 Livestock Management 9
1.3.4 Benefits of Robotics in Agriculture 9
1.3.5 Challenges and Future Prospects 10
1.4 Smart Farming 10
1.5 Technologies Used in AI and IoT for Smart Farming 11
1.5.1 Global Positioning System (GPS) 12
1.5.2 Sensor Technologies 13
1.5.2.1 Environmental Sensors 14
1.6 Energy-Efficient Sustainable Agriculture 14
1.7 Applications in Agriculture 16
1.7.1 Precision Farming 16
1.7.2 Sensors in Farming 17
1.7.3 Soil Mapping and Plant Monitoring 18
1.7.4 Climate Conditions 19
1.7.5 Crop Monitoring and Disease Detection 20
1.7.6 Smart Irrigation and Water Management 21
1.7.7 Environmental Impact and Sustainability 22
1.7.8 Data-Driven Decision-Making 22
1.8 Advantages of IoT and AI in Agriculture 23
1.9 The Future of IoT And AI in Agriculture 24
1.10 Conclusion 25
References 25
2 A Comprehensive Review of Machine Learning Techniques for Smart Grid Optimization 29
Gaurav Gupta, Abhishek Tomar, Saigurudatta Pamulaparthyvenkata and Anandaganesh Balakrishnan
2.1 Introduction 30
2.2 Smart Grid Fundamentals 31
2.2.1 Overview of Smart Grid Technology 31
2.2.2 Key Components and Architecture 31
2.2.3 Challenges in Smart Grid Optimization 32
2.2.4 Role of Machine Learning in Smart Grids 34
2.3 Machine Learning Techniques for Smart Grid Optimization 34
2.3.1 Supervised Learning Approaches 34
2.3.2 Unsupervised Learning Techniques 35
2.3.3 Reinforcement Learning for Dynamic Optimization 35
2.3.4 Deep Learning Applications 36
2.3.5 Hybrid Models and Ensemble Methods 36
2.4 Applications of Machine Learning in Smart Grids 37
2.4.1 Load Forecasting 37
2.4.2 Renewable Energy Integration 38
2.4.3 Predictive Maintenance 39
2.4.4 Energy Management 40
2.4.5 Energy Market Optimization 41
2.5 Advanced Topics in Machine Learning for Smart Grids 43
2.5.1 Explainable AI (XAI) in Smart Grids 43
2.5.1.1 Importance of Explainability 43
2.5.1.2 Techniques for Explainability 44
2.5.2 Transfer Learning for Smart Grid Applications 45
2.5.3 Integration of IoT and Edge Computing 46
2.5.3.1 IoT Devices in Smart Grids 46
2.5.3.2 Edge Computing for Real-Time Analytics 47
2.5.4 Blockchain Technology for Smart Grids 48
2.5.5 AI-Driven Cybersecurity in Smart Grids 49
2.6 Discussion, Future Directions, and Emerging Trends 50
2.6.1 Discussion 50
2.6.2 Future Research Directions and Emerging Trends 51
2.6.3 Practical Implementation Strategies 53
2.7 Conclusion 54
References 55
3 Innovations in Machine Learning for Energy Efficiency: Bridging Predictive Analytics and Real-World Applications 61
Aditya Vardhan, Amarjeet Singh Chauhan, Mohit Yadav, Sanjay Saini and Sagar Sharma
3.1 Introduction 62
3.1.1 Climate and Economic Drivers 63
3.2 Paradigm Shift Toward Decarbonization 65
3.3 Advanced Data-Driven Approaches to Energy Management 66
3.3.1 Big Data Analytics and Energy Management 66
3.3.2 Feature Engineering for Enhanced Predictive Models 67
3.3.3 Spatial-Temporal Analysis for Demand Forecasting 67
3.3.4 Integration of Machine Learning for Dynamic Optimization 67
3.3.5 Hybrid Approaches Combining Data-Driven Techniques 68
3.4 Machine Learning Algorithms for Predictive and Prescriptive Energy Analytics 69
3.4.1 Predictive Analytics: Anticipating Energy Demand and Trends 69
3.4.2 Prescriptive Analytics: Optimizing Energy Management Decisions 70
3.4.3 Hybrid Models for Enhanced Decision-Making 71
3.4.4 Advanced Neural Networks 73
3.5 Optimization Strategies Using Machine Learning 74
3.5.1 Energy Management in Smart Grids with Multi-Agent Systems 75
3.5.2 Optimization Algorithms for Renewable Energy Integration 75
3.5.3 Multi-Objective Optimization in Energy Systems 76
3.6 Cognitive Energy Management Systems 77
3.6.1 Autonomous Energy Management with AI and IoT Integration 77
3.6.2 Self-Learning Systems for Adaptive Energy Efficiency 78
3.6.3 Human-AI Collaboration in Energy Decision Making 78
3.7 Advanced Case Studies and Applications 79
3.7.1 AI-Driven Microgrids for Autonomous Energy Communities 79
3.7.2 AI-Powered Predictive Maintenance in Energy Infrastructure 79
3.7.3 Dynamic Energy Pricing with Machine Learning 80
3.7.4 AI-Driven Smart Building Systems 81
3.7.5 AI in Energy-Intensive Industries 81
3.8 Challenges and Future Directions in ML-Driven Energy Efficiency 83
3.8.1 Ethical and Social Implications of AI in Energy 83
3.8.2 Scalability of ML Models in Large-Scale Energy Systems 83
3.8.3 Future-Proofing Energy Systems with Quantum Machine Learning 84
3.8.4 Cybersecurity Challenges in AI-Driven Energy Systems 84
3.8.5 Regulatory and Compliance Issues 85
3.9 Conclusion 85
References 86
4 Understanding Energy Security Elements and Challenges 93
Priya Batta
4.1 Introduction 93
4.1.1 Important Elements of Energy Security 95
4.1.2 Challenges 97
4.2 Literature Survey 101
4.3 Methodology 104
4.4 Results 106
4.5 Conclusion and Future Scope 107
References 108
5 Energy Storage and Optimization Techniques 111
Mamta
5.1 Introduction 112
5.1.1 Overview of Energy Storage 112
5.1.2 Importance of Optimization in Energy Systems 112
5.1.3 Integration of IoT and AI in Energy Optimization 115
5.2 Fundamentals of Energy Storage 117
5.2.1 Types of Energy Storage Systems 117
5.2.2 Storage Capacity and Characteristics 118
5.3 Rules for Optimization 119
5.3.1 The Basics of Improving Energy Systems 120
5.3.2 Role of IoT in Real-Time Monitoring 121
5.3.3 AI Algorithms for Energy Optimization 122
5.3.3.1 Machine Learning Models 124
5.3.3.2 Deep Learning Approaches 125
5.4 Security Challenges in Energy Optimization 126
5.4.1 Cybersecurity Risks in IoT-Enabled Systems 126
5.4.2 AI-Powered Protecting Energy Optimization Algorithms 127
5.5 New Ideas and Trends for the Future 128
5.5.1 Improvements in Technologies for Storing Energy 128
5.5.2 Emerging Trends in AI for Energy Optimization 129
5.5.3 Sustainable Practices in the Energy Sector 130
5.6 Conclusion 130
References 131
6 IoT-Enabled Energy Storage Systems: Challenges and Solutions 135
Dankan Gowda V., Mirza Shuja, Christian Rafael Quevedo Lezama, Pullela S.V.V.S.R. Kumar and Suganthi N.
6.1 Introduction 136
6.1.1 Overview of Energy Storage Systems (ESS) 136
6.1.2 Role of IoT in Energy Storage 138
6.2 Literature Survey 139
6.2.1 IoT in Monitoring and Diagnostics of Energy Storage Systems 140
6.2.2 Optimization of Energy Storage Operations through IoT 140
6.2.3 Cybersecurity Challenges in IoT-Enabled Energy Storage Systems 141
6.2.4 Case Studies and Real-World Applications of IoT in Energy Storage 142
6.3 Challenges in Energy Storage Systems 143
6.3.1 Integration with Renewable Energy Sources 143
6.3.2 Scalability and Efficiency 146
6.3.3 Security and Privacy Concerns 146
6.4 IoT-Enabled Solutions for Energy Storage Systems 147
6.4.1 Advanced Monitoring and Control 147
6.4.2 Optimization Algorithms 150
6.4.3 Energy Management Systems (EMS) 151
6.4.4 Security Enhancements 152
6.5 Case Studies on Real-Time Applications 153
6.6 Future Trends and Developments 158
6.6.1 Next-Generation IoT Technologies 158
6.6.2 Sustainable and Green Energy Storage Solutions 160
6.7 Conclusion 161
References 161
7 Dynamic Pricing and Energy Optimization Strategies 165
Inderjeet Singh, Muskan Sharma, Suvigya Yadav, Yash Mahajan and Koushik Sundar
7.1 Introduction 166
7.1.1 Fundamentals of Dynamic Pricing 168
7.1.1.1 Understanding Dynamic Pricing Models 168
7.1.2 Economic Principles Behind Dynamic Pricing 171
7.1.2.1 Demand Response Mechanisms 171
7.1.2.2 Price Elasticity of Demand in Energy Markets 172
7.1.2.3 Technological Enablers for Dynamic Pricing 174
7.1.2.4 Smart Meters and Sensors 175
7.1.2.5 IoT-Enabled Energy Consumption Monitoring 176
7.1.3 Integration of IoT and AI for Dynamic Pricing 177
7.1.3.1 Case Studies of Successful Implementations 177
7.1.3.2 Synergies between IoT and AI Technologies 178
7.1.3.3 Data-Driven Energy Management 179
7.1.3.4 Automated Energy Control Systems 182
7.1.4 Challenges and Future Directions 183
7.1.4.1 Data Privacy and Security Concerns 183
7.1.4.2 Regulatory and Policy Considerations 185
7.1.4.3 Future Trends in Dynamic Pricing and Energy Optimization 186
7.1.5 Conclusion 187
7.1.5.1 Implications for the Future of Energy Management 188
7.1.5.2 Vision for a Sustainable and Efficient Energy Future 188
References 189
8 Smart Energy: Harnessing IoT and AI for Renewable Resource Integration 193
Ashutosh Pagrotra
8.1 Introduction 194
8.1.1 The Role of IoT in Renewable Energy 194
8.1.2 Understanding IoT: Definition and Key Components 194
8.1.3 The Key Components of IoT in Renewable Energy Include 194
8.1.4 Enhancing Monitoring and Management of Renewable Energy Systems 195
8.1.5 Optimizing Energy Production and Distribution 196
8.2 Artificial Intelligence in Renewable Energy Management 196
8.2.1 An Overview of AI: Fundamental Ideas and Technologies 197
8.2.2 The Following are Important AI Technologies that are Related to Managing Renewable Energy 197
8.2.3 AI's Function in Energy Generation Forecasting 198
8.2.4 Optimizing Energy Storage with AI 198
8.2.5 Examples of AI-Driven Solutions in Renewable Energy Grids 199
8.3 Smart Grids: The Backbone of IoT and AI in Renewables 200
8.3.1 Definition and Components of Smart Grids 200
8.3.2 Types of Smart Grids are as Follows 200
8.3.3 How Smart Grids Integrate with IoT and AI 201
8.3.4 The Advantages of Smart Grids for Improving Reliability and Energy Efficiency 202
8.3.5 Examples of Smart Grids in Use in the Real World 203
8.4 Data Analytics and Predictive Maintenance in Renewable Systems 204
8.4.1 Importance of Data Analytics in Renewable Energy Systems 205
8.4.2 AI Algorithms and Internet-of-Things Sensors for Predictive Maintenance 206
8.4.3 Predictive Maintenance's Advantages 207
8.4.4 The Future of Predictive Maintenance and Data Analytics in Renewables 207
8.5 Energy Storage Solutions: Optimizing with AI and IoT 208
8.5.1 Overview of Energy Storage Technologies 208
8.5.2 Role of AI and IoT in Maximising Storage Efficiency 209
8.6 Sustainability and Environmental Impact 210
8.6.1 How AI and IoT Help Make Renewable Energy Systems More Sustainable 211
8.6.2 Integration of Distributed Energy Resources (DERs) into the Larger Energy Grid 212
8.6.3 Reducing the Carbon Footprint of Renewable Energy Operations 212
8.6.4 Balancing Technological Advancement with Environmental Stewardship 213
8.7 Future Directions and Research Opportunities 214
8.7.1 New Developments in AI, IoT, and Renewable Energy 214
8.7.2 Research Gaps and Potential Areas for Innovation 215
8.7.3 The Role of Academia, Industry, and Government in Advancing Integration 216
8.8 Conclusion and Key Points 217
8.8.1 Using IoT and AI to Optimize Renewable Energy Systems 217
8.8.2 Improving Energy Management and Grid Stability 218
8.8.3 Predictive Upkeep and Dependability of Systems 218
8.8.4 Applications in the Real World and Case Studies 218
8.8.5 Prospects for Research and Future Paths 219
8.8.6 Working Together for a Sustainable Future 219
References 220
9 Machine Learning Algorithms for Energy Efficiency Enhancement 223
Neetu Rani, Narinder Yadav, Poonam Singh and Vanshika
9.1 Introduction 224
9.1.1 Overview of Energy Efficiency 224
9.1.2 Role of Machine Learning in Energy Efficiency 225
9.1.3 Case Studies and Real-World Applications 225
9.1.4 Objectives of the Chapter 226
9.2 Machine Learning Concepts for Energy Efficiency 227
9.2.1 Supervised Learning 227
9.2.2 Methods of Classification in Energy Efficiency 228
9.2.3 Unsupervised Learning 228
9.2.3.1 Clustering for Load Profiling and Segmentation 228
9.2.3.2 Anomaly Detection in Energy Usage 228
9.3 Reinforcement Learning 229
9.3.1 Role in Dynamic Control Systems for Energy Management 229
9.4 Neural Networks and Deep Learning 230
9.4.1 Applications in Energy Forecasting and Optimization 230
9.4.2 Designing Neural Networks for Energy-Efficient Models 230
9.5 Algorithms for Energy Efficiency Enhancement 230
9.5.1 Linear Regression 230
9.5.2 Decision Trees and Random Forests 231
9.5.3 Support Vector Machines (SVMs) 231
9.5.4 K-Means Clustering 232
9.5.5 Neural Networks and Deep Learning Models 233
9.6 Applications of Machine Learning in Energy Systems 233
9.6.1 Smart Grids 233
9.6.2 Energy Efficiency in Buildings 234
9.6.3 Renewable Energy Systems 235
9.6.4 Transportation and Electric Vehicles 235
9.7 Conclusion 236
9.8 Future Scope 236
References 237
10 Optimizing Vulnerable Energy User Support in England through Clustering Analysis 241
Shola E. Ayeotan and Surbhi Bhatia Khan
10.1 Introduction 241
10.2 Literature Review 243
10.2.1 Determinants of Energy Vulnerability 243
10.2.1.1 Measuring Energy Vulnerability 243
10.2.2 Past Interventions on Social Issues and Ethical Considerations 244
10.2.3 AI in the Energy Sector 245
10.3 Proposed Methodology 245
10.3.1 Data Collection 245
10.3.2 Data Preprocessing 248
10.3.3 Data Transformation 248
10.3.4 Dimensionality Reduction 249
10.3.5 Model Development and Clustering 250
10.3.6 Evaluation and Results Analysis 251
10.4 Model Development and Clustering 251
10.4.1 The Energy Vulnerability Index (EVI) 251
10.4.2 Exploratory Data Analysis (EDA) 253
10.4.3 Clustering with K-Means 260
10.4.4 Clustering with DBSCAN 263
10.4.5 Clustering with HDBSCAN 263
10.5 Result Analysis 266
10.5.1 Cluster Distribution 266
10.5.2 Feature Contribution 267
10.5.3 Spatial Visualization 269
10.5.4 Evaluation 270
10.5.5 Discussions 272
10.6 Conclusion 273
References 273
11 Real-Time Monitoring & Fault Detection in Energy Infrastructure 277
Amit Sharma and Titu Singh Arora
11.1 Introduction 278
11.2 Technologies and Tools for Real-Time Data Acquisition 282
11.3 Data Analytics and Machine Learning for Fault Detection 287
11.4 Case Studies of Real-Time Monitoring Systems in Energy Infrastructure 288
11.5 Integration of IoT and Smart Sensors in Energy Monitoring 291
11.6 Cybersecurity and Data Integrity in Monitoring Systems 293
11.7 Predictive Maintenance with Real-Time Surveillance 296
11.8 Challenges and Solutions in Implementing Fault Detection Systems 296
11.9 Future Trends in Real-Time Monitoring and Fault Detection 297
11.10 Conclusion and Future Research Directions 299
References 299
12 Robust Security Strategies for Smart Grid Networks: Integration of AI, Blockchain, and Resource-Efficient Techniques 303
Santhosh Kumar C., Nancy Lima Christy S., S. Sindhuja and Madhan. K.
12.1 Introduction 304
12.1.1 Background 304
12.1.2 Significance of Security in Smart Grids 304
12.1.3 Overview of Current Security Results 305
12.1.4 Gaps and Challenges in Being Security Results 305
12.1.5 Proposed Security Framework 306
12.1.6 Research Objectives 307
12.1.7 Significance of the Research 307
12.2 Literature Review 309
12.3 Methodology 311
12.3.1 Protocol Selection 312
12.3.1.1 Encryption and Decryption 312
12.3.1.2 Authentication and Authorization 313
12.3.1.3 Intrusion Detection and Prevention Systems (IDPS) 314
12.3.1.4 Secure Communication Protocols 315
12.3.2 Implementation and Integration 315
12.3.2.1 Deployment of Security Measures 315
12.3.2.2 Integration with Existing Systems 316
12.3.2.3 Training and Awareness 316
12.3.3 Evaluation and Testing 317
12.3.3.1 Security Testing 317
12.3.3.2 Performance Evaluation 317
12.3.3.3 Continuous Monitoring and Improvement 317
12.4 Results 318
12.4.1 Encryption Effectiveness 318
12.4.1.1 Encryption Performance 318
12.4.1.2 Encryption Strength 319
12.4.2 Authentication Mechanisms 319
12.4.2.1 Authentication Accuracy 319
12.4.2.2 Authentication Speed 320
12.4.3 Intrusion Detection and Prevention Systems (IDPS) 320
12.4.3.1 Detection Accuracy 320
12.4.3.2 Response Time 321
12.4.4 Overall Performance Impact 321
12.4.4.1 Network Performance 321
12.4.4.2 Resource Utilization 322
12.5 Future Work 323
12.6 Conclusion 324
References 325
13 The Power of Prediction: Revolutionizing Energy Management 327
Neha Bhati, Hardik Dhiman, Surendra Yadav, Rakesh Sharma, Gajendra Shrimal and Jitendra Kumar Katariya
13.1 Introduction 328
13.1.1 Energy System in Building 328
13.1.2 The Transformative Power of Predictive Analytics 328
13.1.3 IoT and AI Transforming Energy Management 329
13.2 Predictive Analytics in Energy Management 330
13.2.1 Properties and Relevance of Predictive Analytics 330
13.2.2 How Predictive Analytics is Changing Energy Management 331
13.2.3 Case Studies of Predictive Analytics Applied in the Real World 332
13.2.4 Combine IoT and AI for Predictive Energy Management 332
13.2.5 Case Studies of IoT- and AI-Based Prediction Systems 333
13.2.6 Benefits of Integrating these Technologies 334
13.3 Problems with Deploying Predictive Energy Management 335
13.3.1 Data-Related Challenges: Quality, Availability, and Security 335
13.3.2 Physical and Integration Hurdles 337
13.3.3 Regulatory and Ethical Issues 337
13.4 Case Studies and Applications 338
13.4.1 Examples in Detail of Different Sectors (i.e., Residential, Industrial, and Commercial) 338
13.4.2 Analysis of Successful Implementations and their Impact 340
13.4.3 Lessons Learned from Real-World Applications 341
13.5 Future Trends in Predictive Energy Management 341
13.5.1 Emerging Technologies and their Potential Impact 343
13.5.2 AI and IoT in Energy Management Future Directions 343
13.5.3 Predictions for the Evolution of Energy Management Over the Next Decade 345
13.6 Conclusion 346
13.6.1 Summary of the Key Insights 346
13.6.2 The Potential of Predictive Analytics to Revolutionize Energy Management 346
13.6.3 Conclusions on the Future of the Field 347
References 347
14 Predictive Analytics as a Pathway to Intelligent Demand Response and Load Management 351
Aditya Vardhan, Amarjeet Singh Chauhan and Sagar Sharma
14.1 Introduction 352
14.1.1 Enhance Demand Forecasting and Optimized Load Management 353
14.1.2 Improved Demand Response 353
14.1.3 Enhanced Integration of Renewable Energy 354
14.1.4 Consumer Engagement 354
14.2 Overview of Predictive Analytics 354
14.3 Demand Purpose 355
14.3.1 Forecasting Demand 355
14.3.2 Customer Segmentation and Behavior Analysis 357
14.3.3 Demand Response Strategies 359
14.4 Load Management 361
14.4.1 Forecasting and Optimization 361
14.4.2 Demand-Side Management 362
14.4.3 Capacity Planning 363
14.4.4 Load Shedding and Peak Shaving 363
14.5 Technologies and Tools: Fundamental Requirements 363
14.5.1 Machine Learning Algorithms 364
14.5.2 Big Data and Internet of Things 364
14.5.3 Software and Platform 365
14.5.4 Advanced Analytical Techniques 365
14.6 Advanced Demand Response and Load Management Strategies 366
14.6.1 Innovative Incentive Structures 367
14.6.2 Real-Time Load Management Techniques 368
14.6.3 Advanced Capacity Planning 368
14.6.4 Behavioral Demand Response Innovation 369
14.7 Challenges and Limitations 370
14.7.1 Data Quality and Availability 370
14.7.2 Model Accuracy and Complexity 370
14.7.3 Privacy and Security 371
14.8 Practical Applications of Predictive Analytics 371
14.8.1 Enhanced Grid Management 371
14.8.2 Renewable Energy Integration 372
14.8.3 Advanced Load Management 372
14.9 Emerging Patterns and New Directions 372
14.9.1 Integration of Artificial Intelligence (AI) and Machine Learning (ML) 372
14.9.2 Focus on Consumer-Centric Solutions 373
14.9.3 Factors Related to Regulation and Compliance 373
14.9.4 Future Prognostication 373
14.10 Conclusion 374
References 375
15 Data Collection and Analysis for Real-Time Secure Energy Monitoring and Optimization 381
Dankan Gowda V., Pullela S.V.V.S.R. Kumar, Madan Mohanrao Jagtap, Shekhar R. and Rahul Vadisetty
15.1 Introduction 382
15.2 Literature Survey 384
15.2.1 Evolution of Energy Monitoring Systems 384
15.2.2 Real-Time Data Collection and Analysis 384
15.2.3 Security Challenges in Energy Monitoring Systems 385
15.2.4 Emerging Trends in Energy Monitoring and Optimization 385
15.3 Fundamentals of Real-Time Energy Monitoring 386
15.4 Data Collection Techniques for Energy Monitoring 388
15.4.1 Types of Data in Energy Systems 390
15.4.2 Data Acquisition Methods and Devices 391
15.4.3 Communication Protocols 392
15.4.4 Challenges in Real-Time Data Collection 393
15.5 Data Analysis for Energy Optimization 394
15.5.1 Introduction to Data Analysis Techniques 396
15.5.2 Real-Time Data Processing Frameworks 397
15.5.3 Predictive Maintenance and Anomaly Detection in Energy Systems 398
15.5.4 Load Forecasting and Demand-Side Management 398
15.5.5 Security Considerations in Real-Time Energy Monitoring 399
15.5.6 Overview of Security Threats in Energy Monitoring Systems 401
15.5.7 Encryption Techniques for Securing Data Transmission 401
15.5.8 Secure Data Storage and Access Control 402
15.5.9 Blockchain Technology for Secure Energy Transactions 402
15.6 Case Studies 403
15.7 Future Trends in Real-Time Secure Energy Monitoring 408
15.7.1 Emerging Technologies in Energy Monitoring 408
15.8 Conclusion 409
References 410
16 Methods for Implementing Real-Time Pricing and Improving Energy Efficiency 413
Amit Sharma
16.1 Introduction 414
16.2 Overview of Real-Time Pricing (RTP) 418
16.3 Fundamentals of Real-Time Pricing 419
16.4 Technological Requirements for Real-Time Pricing Implementation 422
16.5 Strategies for Effective Real-Time Pricing 424
16.6 Consumer Engagement and Behavioral Insights 427
16.7 Energy Efficiency Improvement Techniques 430
16.8 Energy Efficiency Improvement Techniques 432
References 434
17 Case Studies: Successful Implementations of Secure Energy Optimization Using IoT and AI 437
Saritakumar N., Sudharsan M. K., Gowthaman S., Sreeman T. S. and Harrish Sridhar
17.1 Introduction 438
17.1.1 Background and Context 438
17.1.2 Purpose of the Study 439
17.1.3 Challenges in Securing IoT and AI in Energy Systems 441
17.2 Literature Review 442
17.3 Methodology 443
17.4 Case Studies 445
17.4.1 Case Study 1: Optimizing Wind Turbine Maintenance Using AI 445
17.4.2 Case Study 2: Smart Grid Optimization with AI and IoT 447
17.4.3 Case Study 3: AI-Driven Energy Management in Smart Homes 448
17.4.4 Case Study 4: AI for Predictive Maintenance in Solar Power Plants 450
17.5 Analysis and Discussion 453
17.5.1 Comparison of Case Studies 453
17.5.2 Challenges and Solutions 454
17.6 Conclusion 455
References 456
About the Editors 459
Index 463
