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Secure Energy Optimization

Leveraging Internet of Things and Artificial Intelligence for Enhanced Efficiency
Edited by Abhishek Kumar, Surbhi Bhatia Khan, Narayan Vyas, Vishal Dutt, and Shakila Basheer
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394271818  |  Hardcover  |  
492 pages
Price: $225 USD
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One Line Description
Secure Energy Optimization: Leveraging Internet of Things and Artificial Intelligence for Enhanced Efficiency is essential for anyone looking to navigate the transformative landscape of energy management, as it expertly combines the principles of IoT and AI with real-world case studies to provide actionable insights for achieving sustainable and efficient energy optimization.

Audience
Energy professionals, IoT and AI specialists, policymakers, researchers, students, consultants, technology providers, and environmental advocates interested in the convergence of IoT, AI, and energy optimization.

Description
Energy is rapidly changing, with an emphasis on sustainable and efficient energy use. In this context, the combination of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has emerged as a potent technique for optimising energy use, improving efficiency, and enhancing overall energy security. Secure Energy Optimization: Leveraging Internet of Things and Artificial Intelligence for Enhanced Efficiency provides a comprehensive review of how IoT and AI can be used to accomplish safe energy optimisation. Readers will gain an understanding of the underlying principles of IoT and AI, as well as their applications in energy efficiency and the problems and hazards related to their adoption. They will investigate the successful integration of IoT and AI technologies in energy management systems, smart grids, and renewable energy sources using real-world case studies and examples. By bringing together theoretical notions, cutting-edge research, and practical examples, this book bridges the gap between theory and implementation.

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Author / Editor Details
Abhishek Kumar, PhD is an assistant professor and the Research and Development Coordinator at Chitkara University with over 11 years of experience. He has over 100 publications in peer-reviewed national and international journals, books, and conferences. His research includes artificial intelligence, renewable energy, image processing, computer vision, data mining, and machine Learning.

Surbhi Bhatia Khan, PhD is a lecturer in the Department of Data Science in the School of Science, Engineering, and Environment at the University of Salford with over 11 years of teaching experience. She has published over 100 papers in reputed journals, 12 international patents, and 12 books. Her areas of interest include information systems, sentiment analysis, machine learning, databases, and data science.

Narayan Vyas is a principal research consultant at AVN Innovations, where he is actively involved in research and development. He has published many articles in reputed, peer-reviewed national and international journals and conferences. His research areas include the Internet of Things, machine learning, deep learning, computer vision, and bioinformatics.

Vishal Dutt is a technical trainer in the Department of Computer Science and Engineering at Chandigarh University with over seven years of teaching experience. He has over 50 publications in reputed, peer-reviewed national and international journals, conferences, and book chapters, in addition to two books. His research interests include data science, data mining, machine learning, and deep learning.

Shakila Basheer, PhD is an assistant professor in the Department of Information Systems in the College of Computer and Information Science at Princess Nourah Bint Abdulrahman University with over ten years of teaching experience. She has published over 90 technical papers in international journals, conferences, and book chapters. Her research focus includes data mining, image processing, and fuzzy logic.

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Table of Contents
Preface
1. Exploring the IoT and AI Technologies in Energy-Efficient Sustainable Agriculture
M. Muthumalathi, Ponnarasi Loganathan, P.B. Pankajavalli and A. Priya Dharshini
1.1 Introduction to the Internet of Things and Artificial Intelligence
1.2 Essentials of IoT and AI Applications in Agriculture
1.2.1 IoT Applications in Agriculture
1.2.1.1 Monitor and Sense
1.2.1.2 IoT-Enabled Precision Agriculture
1.2.1.3 Monitor Livestock
1.2.1.4 Managing the Supply Chain
1.2.2 AI Applications in Agriculture
1.2.2.1 Soil Management
1.2.2.2 Weed Management
1.3 Robotics in Agriculture
1.3.1 Automation of Agricultural Processes
1.3.2 Precision Farming
1.3.3 Livestock Management
1.3.4 Benefits of Robotics in Agriculture
1.3.5 Challenges and Future Prospects
1.4 Smart Farming
1.5 Technologies Used in AI and IoT for Smart Farming
1.5.1 Global Positioning System (GPS)
1.5.2 Sensor Technologies
1.5.2.1 Environmental Sensors
1.6 Energy-Efficient Sustainable Agriculture
1.7 Applications in Agriculture
1.7.1 Precision Farming
1.7.2 Sensors in Farming
1.7.3 Soil Mapping and Plant Monitoring
1.7.4 Climate Conditions
1.7.5 Crop Monitoring and Disease Detection
1.7.6 Smart Irrigation and Water Management
1.7.7 Environmental Impact and Sustainability
1.7.8 Data-Driven Decision-Making
1.8 Advantages of IoT and AI in Agriculture
1.9 The Future of IoT And AI in Agriculture
1.10 Conclusion
References
2. A Comprehensive Review of Machine Learning Techniques for Smart Grid Optimization
Gaurav Gupta, Abhishek Tomar, Saigurudatta Pamulaparthyvenkata and Anandaganesh Balakrishnan
2.1 Introduction
2.2 Smart Grid Fundamentals
2.2.1 Overview of Smart Grid Technology
2.2.2 Key Components and Architecture
2.2.3 Challenges in Smart Grid Optimization
2.2.4 Role of Machine Learning in Smart Grids
2.3 Machine Learning Techniques for Smart Grid Optimization
2.3.1 Supervised Learning Approaches
2.3.2 Unsupervised Learning Techniques
2.3.3 Reinforcement Learning for Dynamic Optimization
2.3.4 Deep Learning Applications
2.3.5 Hybrid Models and Ensemble Methods
2.4 Applications of Machine Learning in Smart Grids
2.4.1 Load Forecasting
2.4.2 Renewable Energy Integration
2.4.3 Predictive Maintenance
2.4.4 Energy Management
2.4.5 Energy Market Optimization
2.5 Advanced Topics in Machine Learning for Smart Grids
2.5.1 Explainable AI (XAI) in Smart Grids
2.5.1.1 Importance of Explainability
2.5.1.2 Techniques for Explainability
2.5.2 Transfer Learning for Smart Grid Applications
2.5.3 Integration of IoT and Edge Computing
2.5.3.1 IoT Devices in Smart Grids
2.5.3.2 Edge Computing for Real-Time Analytics
2.5.4 Blockchain Technology for Smart Grids
2.5.5 AI-Driven Cybersecurity in Smart Grids
2.6 Discussion, Future Directions, and Emerging Trends
2.6.1 Discussion
2.6.2 Future Research Directions and Emerging Trends
2.6.3 Practical Implementation Strategies
2.7 Conclusion
References
3. Innovations in Machine Learning for Energy Efficiency: Bridging Predictive Analytics and Real-World Applications
Aditya Vardhan, Amarjeet Singh Chauhan, Mohit Yadav, Sanjay Saini and Sagar Sharma
3.1 Introduction
3.1.1 Climate and Economic Drivers
3.2 Paradigm Shift Toward Decarbonization
3.3 Advanced Data-Driven Approaches to Energy Management
3.3.1 Big Data Analytics and Energy Management
3.3.2 Feature Engineering for Enhanced Predictive Models
3.3.3 Spatial-Temporal Analysis for Demand Forecasting
3.3.4 Integration of Machine Learning for Dynamic Optimization
3.3.5 Hybrid Approaches Combining Data-Driven Techniques
3.4 Machine Learning Algorithms for Predictive and Prescriptive Energy Analytics
3.4.1 Predictive Analytics: Anticipating Energy Demand and Trends
3.4.2 Prescriptive Analytics: Optimizing Energy Management Decisions
3.4.3 Hybrid Models for Enhanced Decision-Making
3.4.4 Advanced Neural Networks
3.5 Optimization Strategies Using Machine Learning
3.5.1 Energy Management in Smart Grids with Multi-Agent Systems
3.5.2 Optimization Algorithms for Renewable Energy Integration
3.5.3 Multi-Objective Optimization in Energy Systems
3.6 Cognitive Energy Management Systems
3.6.1 Autonomous Energy Management with AI and IoT Integration
3.6.2 Self-Learning Systems for Adaptive Energy Efficiency
3.6.3 Human-AI Collaboration in Energy Decision Making
3.7 Advanced Case Studies and Applications
3.7.1 AI-Driven Microgrids for Autonomous Energy Communities
3.7.2 AI-Powered Predictive Maintenance in Energy Infrastructure
3.7.3 Dynamic Energy Pricing with Machine Learning
3.7.4 AI-Driven Smart Building Systems
3.7.5 AI in Energy-Intensive Industries
3.8 Challenges and Future Directions in ML-Driven Energy Efficiency
3.8.1 Ethical and Social Implications of AI in Energy
3.8.2 Scalability of ML Models in Large-Scale Energy Systems
3.8.3 Future-Proofing Energy Systems with Quantum Machine Learning
3.8.4 Cybersecurity Challenges in AI-Driven Energy Systems
3.8.5 Regulatory and Compliance Issues
3.9 Conclusion
References
4. Understanding Energy Security Elements and Challenges
Priya Batta
4.1 Introduction
4.1.1 Important Elements of Energy Security
4.1.2 Challenges
4.2 Literature Survey
4.3 Methodology
4.4 Results
4.5 Conclusion and Future Scope
References
5. Energy Storage and Optimization Techniques
Mamta
5.1 Introduction
5.1.1 Overview of Energy Storage
5.1.2 Importance of Optimization in Energy Systems
5.1.3 Integration of IoT and AI in Energy Optimization
5.2 Fundamentals of Energy Storage
5.2.1 Types of Energy Storage Systems
5.2.2 Storage Capacity and Characteristics
5.3 Rules for Optimization
5.3.1 The Basics of Improving Energy Systems
5.3.2 Role of IoT in Real-Time Monitoring
5.3.3 AI Algorithms for Energy Optimization
5.3.3.1 Machine Learning Models
5.3.3.2 Deep Learning Approaches
5.4 Security Challenges in Energy Optimization
5.4.1 Cybersecurity Risks in IoT-Enabled Systems
5.4.2 AI-Powered Protecting Energy Optimization Algorithms
5.5 New Ideas and Trends for the Future
5.5.1 Improvements in Technologies for Storing Energy
5.5.2 Emerging Trends in AI for Energy Optimization
5.5.3 Sustainable Practices in the Energy Sector
5.6 Conclusion
References
6. IoT-Enabled Energy Storage Systems: Challenges and Solutions
Dankan Gowda V., Mirza Shuja, Christian Rafael Quevedo Lezama, Pullela S.V.V.S.R. Kumar and Suganthi N.
6.1 Introduction
6.1.1 Overview of Energy Storage Systems (ESS)
6.1.2 Role of IoT in Energy Storage
6.2 Literature Survey
6.2.1 IoT in Monitoring and Diagnostics of Energy Storage Systems
6.2.2 Optimization of Energy Storage Operations through IoT
6.2.3 Cybersecurity Challenges in IoT-Enabled Energy Storage Systems
6.2.4 Case Studies and Real-World Applications of IoT in Energy Storage
6.3 Challenges in Energy Storage Systems
6.3.1 Integration with Renewable Energy Sources
6.3.2 Scalability and Efficiency
6.3.3 Security and Privacy Concerns
6.4 IoT-Enabled Solutions for Energy Storage Systems
6.4.1 Advanced Monitoring and Control
6.4.2 Optimization Algorithms
6.4.3 Energy Management Systems (EMS)
6.4.4 Security Enhancements
6.5 Case Studies on Real-Time Applications
6.6 Future Trends and Developments
6.6.1 Next-Generation IoT Technologies
6.6.2 Sustainable and Green Energy Storage Solutions
6.7 Conclusion
References
7. Dynamic Pricing and Energy Optimization Strategies
Inderjeet Singh, Muskan Sharma, Suvigya Yadav, Yash Mahajan and Koushik Sundar
7.1 Introduction
7.1.1 Fundamentals of Dynamic Pricing
7.1.1.1 Understanding Dynamic Pricing Models
7.1.2 Economic Principles Behind Dynamic Pricing
7.1.2.1 Demand Response Mechanisms
7.1.2.2 Price Elasticity of Demand in Energy Markets
7.1.2.3 Technological Enablers for Dynamic Pricing
7.1.2.4 Smart Meters and Sensors
7.1.2.5 IoT-Enabled Energy Consumption Monitoring
7.1.3 Integration of IoT and AI for Dynamic Pricing
7.1.3.1 Case Studies of Successful Implementations
7.1.3.2 Synergies between IoT and AI Technologies
7.1.3.3 Data-Driven Energy Management
7.1.3.4 Automated Energy Control Systems
7.1.4 Challenges and Future Directions
7.1.4.1 Data Privacy and Security Concerns
7.1.4.2 Regulatory and Policy Considerations
7.1.4.3 Future Trends in Dynamic Pricing and Energy Optimization
7.1.5 Conclusion
7.1.5.1 Implications for the Future of Energy Management
7.1.5.2 Vision for a Sustainable and Efficient Energy Future
References
8. Smart Energy: Harnessing IoT and AI for Renewable Resource Integration
Ashutosh Pagrotra
8.1 Introduction
8.1.1 The Role of IoT in Renewable Energy
8.1.2 Understanding IoT: Definition and Key Components
8.1.3 The Key Components of IoT in Renewable Energy Include
8.1.4 Enhancing Monitoring and Management of Renewable Energy Systems
8.1.5 Optimizing Energy Production and Distribution
8.2 Artificial Intelligence in Renewable Energy Management
8.2.1 An Overview of AI: Fundamental Ideas and Technologies
8.2.2 The Following are Important AI Technologies that are Related to Managing Renewable Energy
8.2.3 AI’s Function in Energy Generation Forecasting
8.2.4 Optimizing Energy Storage with AI
8.2.5 Examples of AI-Driven Solutions in Renewable Energy Grids
8.3 Smart Grids: The Backbone of IoT and AI in Renewables
8.3.1 Definition and Components of Smart Grids
8.3.2 Types of Smart Grids are as Follows
8.3.3 How Smart Grids Integrate with IoT and AI
8.3.4 The Advantages of Smart Grids for Improving Reliability and Energy Efficiency
8.3.5 Examples of Smart Grids in Use in the Real World
8.4 Data Analytics and Predictive Maintenance in Renewable Systems
8.4.1 Importance of Data Analytics in Renewable Energy Systems
8.4.2 AI Algorithms and Internet-of-Things Sensors for Predictive Maintenance
8.4.3 Predictive Maintenance’s Advantages
8.4.4 The Future of Predictive Maintenance and Data Analytics in Renewables
8.5 Energy Storage Solutions: Optimizing with AI and IoT
8.5.1 Overview of Energy Storage Technologies
8.5.2 Role of AI and IoT in Maximising Storage Efficiency
8.6 Sustainability and Environmental Impact
8.6.1 How AI and IoT Help Make Renewable Energy Systems More Sustainable
8.6.2 Integration of Distributed Energy Resources (DERs) into the Larger Energy Grid
8.6.3 Reducing the Carbon Footprint of Renewable Energy Operations
8.6.4 Balancing Technological Advancement with Environmental Stewardship
8.7 Future Directions and Research Opportunities
8.7.1 New Developments in AI, IoT, and Renewable Energy
8.7.2 Research Gaps and Potential Areas for Innovation
8.7.3 The Role of Academia, Industry, and Government in Advancing Integration
8.8 Conclusion and Key Points
8.8.1 Using IoT and AI to Optimize Renewable Energy Systems
8.8.2 Improving Energy Management and Grid Stability
8.8.3 Predictive Upkeep and Dependability of Systems
8.8.4 Applications in the Real World and Case Studies
8.8.5 Prospects for Research and Future Paths
8.8.6 Working Together for a Sustainable Future
References
9. Machine Learning Algorithms for Energy Efficiency Enhancement
Neetu Rani, Narinder Yadav, Poonam Singh and Vanshika
9.1 Introduction
9.1.1 Overview of Energy Efficiency
9.1.2 Role of Machine Learning in Energy Efficiency
9.1.3 Case Studies and Real-World Applications
9.1.4 Objectives of the Chapter
9.2 Machine Learning Concepts for Energy Efficiency
9.2.1 Supervised Learning
9.2.2 Unsupervised Learning
9.2.3 Reinforcement Learning
9.2.4 Neural Networks and Deep Learning
9.3 Algorithms for Energy Efficiency Enhancement
9.3.1 Linear Regression
9.3.2 Decision Trees and Random Forests
9.3.3 Support Vector Machines (SVMs)
9.3.4 K-Means Clustering
9.3.5 Neural Networks and Deep Learning Models
9.4 Applications of Machine Learning in Energy Systems
9.4.1 Smart Grids
9.4.2 Energy Efficiency in Buildings
9.4.3 Renewable Energy Systems
9.4.4 Transportation and Electric Vehicles
9.5 Conclusion
9.6 Future Scope
References
10. Optimizing Vulnerable Energy User Support in England through Clustering Analysis
Shola E. Ayeotan and Surbhi Bhatia Khan
10.1 Introduction
10.2 Literature Review
10.2.1 Determinants of Energy Vulnerability
10.2.1.1 Measuring Energy Vulnerability
10.2.2 Past Interventions on Social Issues and Ethical Considerations
10.2.3 AI in the Energy Sector
10.3 Proposed Methodology
10.3.1 Data Collection
10.3.2 Data Preprocessing
10.3.3 Data Transformation
10.3.4 Dimensionality Reduction
10.3.5 Model Development and Clustering
10.3.6 Evaluation and Results Analysis
10.4 Model Development and Clustering
10.4.1 The Energy Vulnerability Index (EVI)
10.4.2 Exploratory Data Analysis (EDA)
10.4.3 Clustering with K-Means
10.4.4 Clustering with DBSCAN
10.4.5 Clustering with HDBSCAN
10.5 Result Analysis
10.5.1 Cluster Distribution
10.5.2 Feature Contribution
10.5.3 Spatial Visualization
10.5.4 Evaluation
10.5.5 Discussions
10.6 Conclusion
References
11. Real-Time Monitoring & Fault Detection in Energy Infrastructure
Amit Sharma and Titu Singh Arora
11.1 Introduction
11.2 Technologies and Tools for Real-Time Data Acquisition
11.3 Data Analytics and Machine Learning for Fault Detection
11.4 Case Studies of Real-Time Monitoring Systems in Energy Infrastructure
11.5 Integration of IoT and Smart Sensors in Energy Monitoring
11.6 Cybersecurity and Data Integrity in Monitoring Systems
11.7 Predictive Maintenance with Real-Time Surveillance
11.8 Challenges and Solutions in Implementing Fault Detection Systems
11.9 Future Trends in Real-Time Monitoring and Fault Detection
11.10 Conclusion and Future Research Directions
References
12. Robust Security Strategies for Smart Grid Networks: Integration of AI, Blockchain, and Resource-Efficient Techniques
Santhosh Kumar C., Nancy Lima Christy S., S. Sindhuja and Madhan. K.
12.1 Introduction
12.1.1 Background
12.1.2 Significance of Security in Smart Grids
12.1.3 Overview of Current Security Results
12.1.4 Gaps and Challenges in Being Security Results
12.1.5 Proposed Security Framework
12.1.6 Research Objectives
12.1.7 Significance of the Research
12.2 Literature Review
12.3 Methodology
12.3.1 Protocol Selection
12.3.1.1 Encryption and Decryption
12.3.1.2 Authentication and Authorization
12.3.1.3 Intrusion Detection and Prevention Systems (IDPS)
12.3.1.4 Secure Communication Protocols
12.3.2 Implementation and Integration
12.3.2.1 Deployment of Security Measures
12.3.2.2 Integration with Existing Systems
12.3.2.3 Training and Awareness
12.3.3 Evaluation and Testing
12.3.3.1 Security Testing
12.3.3.2 Performance Evaluation
12.3.3.3 Continuous Monitoring and Improvement
12.4 Results
12.4.1 Encryption Effectiveness
12.4.1.1 Encryption Performance
12.4.1.2 Encryption Strength
12.4.2 Authentication Mechanisms
12.4.2.1 Authentication Accuracy
12.4.2.2 Authentication Speed
12.4.3 Intrusion Detection and Prevention Systems (IDPS)
12.4.3.1 Detection Accuracy
12.4.3.2 Response Time
12.4.4 Overall Performance Impact
12.4.4.1 Network Performance
12.4.4.2 Resource Utilization
12.5 Future Work
12.6 Conclusion
References
13. The Power of Prediction: Revolutionizing Energy Management
Neha Bhati, Hardik Dhiman, Surendra Yadav, Rakesh Sharma, Gajendra Shrimal and Jitendra Kumar Katariya
13.1 Introduction
13.1.1 Energy System in Building
13.1.2 The Transformative Power of Predictive Analytics
13.1.3 IoT and AI Transforming Energy Management
13.2 Predictive Analytics in Energy Management
13.2.1 Properties and Relevance of Predictive Analytics
13.2.2 How Predictive Analytics is Changing Energy Management
13.2.3 Case Studies of Predictive Analytics Applied in the Real World
13.2.4 Combine IoT and AI for Predictive Energy Management
13.2.5 Case Studies of IoT- and AI-Based Prediction Systems
13.2.6 Benefits of Integrating these Technologies
13.3 Problems with Deploying Predictive Energy Management
13.3.1 Data-Related Challenges: Quality, Availability, and Security
13.3.2 Physical and Integration Hurdles
13.3.3 Regulatory and Ethical Issues
13.4 Case Studies and Applications
13.4.1 Examples in Detail of Different Sectors (i.e., Residential, Industrial, and Commercial)
13.4.2 Analysis of Successful Implementations and their Impact
13.4.3 Lessons Learned from Real-World Applications
13.5 Future Trends in Predictive Energy Management
13.5.1 Emerging Technologies and their Potential Impact
13.5.2 AI and IoT in Energy Management Future Directions
13.5.3 Predictions for the Evolution of Energy Management Over the Next Decade
13.6 Conclusion
13.6.1 Summary of the Key Insights
13.6.2 The Potential of Predictive Analytics to Revolutionize Energy Management
13.6.3 Conclusions on the Future of the Field
References
14. Predictive Analytics as a Pathway to Intelligent Demand Response and Load Management
Aditya Vardhan, Amarjeet Singh Chauhan and Sagar Sharma
14.1 Introduction
14.1.1 Enhance Demand Forecasting and Optimized Load Management
14.1.2 Improved Demand Response
14.1.3 Enhanced Integration of Renewable Energy
14.1.4 Consumer Engagement
14.2 Overview of Predictive Analytics
14.3 Demand Purpose
14.3.1 Forecasting Demand
14.3.2 Customer Segmentation and Behavior Analysis
14.3.3 Demand Response Strategies
14.4 Load Management
14.4.1 Forecasting and Optimization
14.4.2 Demand-Side Management
14.4.3 Capacity Planning
14.4.4 Load Shedding and Peak Shaving
14.5 Technologies and Tools: Fundamental Requirements
14.5.1 Machine Learning Algorithms
14.5.2 Big Data and Internet of Things
14.5.3 Software and Platform
14.5.4 Advanced Analytical Techniques
14.6 Advanced Demand Response and Load Management Strategies
14.6.1 Innovative Incentive Structures
14.6.2 Real-Time Load Management Techniques
14.6.3 Advanced Capacity Planning
14.6.4 Behavioral Demand Response Innovation
14.7 Challenges and Limitations
14.7.1 Data Quality and Availability
14.7.2 Model Accuracy and Complexity
14.7.3 Privacy and Security
14.8 Practical Applications of Predictive Analytics
14.8.1 Enhanced Grid Management
14.8.2 Renewable Energy Integration
14.8.3 Advanced Load Management
14.9 Emerging Patterns and New Directions
14.9.1 Integration of Artificial Intelligence (AI) and Machine Learning (ML)
14.9.2 Focus on Consumer-Centric Solutions
14.9.3 Factors Related to Regulation and Compliance
14.9.4 Future Prognostication
14.10 Conclusion
References
15. Data Collection and Analysis for Real-Time Secure Energy Monitoring and Optimization
Dankan Gowda V., Pullela S.V.V.S.R. Kumar, Madan Mohanrao Jagtap, Shekhar R. and Rahul Vadisetty
15.1 Introduction
15.2 Literature Survey
15.2.1 Evolution of Energy Monitoring Systems
15.2.2 Real-Time Data Collection and Analysis
15.2.3 Security Challenges in Energy Monitoring Systems
15.2.4 Emerging Trends in Energy Monitoring and Optimization
15.3 Fundamentals of Real-Time Energy Monitoring
15.4 Data Collection Techniques for Energy Monitoring
15.4.1 Types of Data in Energy Systems
15.4.2 Data Acquisition Methods and Devices
15.4.3 Communication Protocols
15.4.4 Challenges in Real-Time Data Collection
15.5 Data Analysis for Energy Optimization
15.5.1 Introduction to Data Analysis Techniques
15.5.2 Real-Time Data Processing Frameworks
15.5.3 Predictive Maintenance and Anomaly Detection in Energy Systems
15.5.4 Load Forecasting and Demand-Side Management
15.5.5 Security Considerations in Real-Time Energy Monitoring
15.5.6 Overview of Security Threats in Energy Monitoring Systems
15.5.7 Encryption Techniques for Securing Data Transmission
15.5.8 Secure Data Storage and Access Control
15.5.9 Blockchain Technology for Secure Energy Transactions
15.6 Case Studies
15.7 Future Trends in Real-Time Secure Energy Monitoring
15.7.1 Emerging Technologies in Energy Monitoring
15.8 Conclusion
References
16. Methods for Implementing Real-Time Pricing and Improving Energy Efficiency
Amit Sharma
16.1 Introduction
16.2 Overview of Real-Time Pricing (RTP)
16.3 Fundamentals of Real-Time Pricing
16.4 Technological Requirements for Real-Time Pricing Implementation
16.5 Strategies for Effective Real-Time Pricing
16.6 Consumer Engagement and Behavioral Insights
16.7 Energy Efficiency Improvement Techniques
16.8 Energy Efficiency Improvement Techniques
References
17. Case Studies: Successful Implementations of Secure Energy Optimization Using IoT and AI
Saritakumar N., Sudharsan M. K., Gowthaman S., Sreeman T. S. and Harrish Sridhar
17.1 Introduction
17.1.1 Background and Context
17.1.2 Purpose of the Study
17.1.3 Challenges in Securing IoT and AI in Energy Systems
17.2 Literature Review
17.3 Methodology
17.4 Case Studies
17.4.1 Case Study 1: Optimizing Wind Turbine Maintenance Using AI
17.4.2 Case Study 2: Smart Grid Optimization with AI and IoT
17.4.3 Case Study 3: AI-Driven Energy Management in Smart Homes
17.4.4 Case Study 4: AI for Predictive Maintenance in Solar Power Plants
17.5 Analysis and Discussion
17.5.1 Comparison of Case Studies
17.5.2 Challenges and Solutions
17.6 Conclusion
References
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