Preface
About This Book
Key Features
Contents
About the Editors
1 Data Analytics for Smart Grids and Applications—Present and Future Directions
1.1 Introduction
1.2 Literature Review
1.3 Smart Grid Infrastructure
1.4 Data Analytics in Smart Grids
1.4.1 Data Pre Processing Techniques in Smart Grids
1.4.2 Case Study of Data Analytics in Smart Grids
1.5 Artificial Intelligence in Smart Grids
1.5.1 Event Detection Using Data Analytics and Cloud Computing for Intelligent IoT System
1.6 Conclusion
References
2 Design, Optimization and Performance Analysis of Microgrids Using Multi-agent Q-Learning
2.1 Introduction
2.2 Literature Review
2.3 Proposed Model
2.4 Experiments
2.5 Conclusion
References
3 Big Data Analytics for Smart Grid: A Review on State-of-Art Techniques and Future Directions
3.1 Introduction
3.2 State-of-Art Techniques for Big Data Analytics in Smart Grids
3.3 Challenges in Big Data Analytics for Smart Grids
3.4 Big Data Analytics for Smart Grids
3.5 Applications of Big Data Analytics in Smart Grids
3.6 Challenges and Future Directions for Big Data Analytics in Smart Grids
3.7 Case Studies of Big Data Analytics in Smart Grids
3.7.1 Case Study 1: Duke Energy's Grid Modernization Program
3.7.2 Case Study 2: National Grid's Smart Grid Program
3.7.3 Case Study 3: ENEL's Smart Grid Program
3.8 Future Directions for Big Data Analytics in Smart Grids
3.9 Real-Time Big Data Analytics for Smart Grids
3.10 Conclusion
References
4 Smart Grid Management for Smart City Infrastructure Using Wearable Sensors
4.1 Introduction
4.1.1 Smart Grid Versus Traditional Electricity Grids
4.1.2 Why Do We Need Smart Grids?
4.1.3 Smart Grid Features
4.1.4 Smart Grid Technologies
4.1.5 Smart Grid Approaches
4.1.6 Smart Meters and Home EMS
4.1.7 Smart Appliances
4.1.8 Home Power Generation
4.1.9 Machine Learning for Data Analytics in Smart Grids and Energy Management
4.1.10 Security for Industrial Control Systems in Smart Grids
4.1.11 Power Flow Modelling and Optimization in Smart Grids
4.1.12 Grid Stability and Security in Smart Grids
4.1.13 Integration of Renewable Energy Sources in Smart Grid Management
4.1.14 Demand Response Strategies for Efficient Smart Grid Management
4.1.15 Cybersecurity Measures for Smart Grid Management
4.1.16 Energy Storage Systems and Their Role in Smart Grid Management
4.1.17 Data Analytics and Artificial Intelligence in Smart Grid Management
4.1.18 Smart Grid Communication Protocols and Infrastructure
4.1.19 Advantages of Smart Grids
4.1.20 Disadvantages of Smart Grids
4.2 Conclusion
References
5 Studies on Conventional and Advanced Machine Learning Algorithm Towards Framing of Robust Data Analytics for the Smart Grid Application
5.1 Introduction
5.2 Review of Different Smart Grid Based Approaches
5.3 Smart Grid Model
5.3.1 Smart Grids as Coordinators for Data Flow and Energy Flow
5.3.2 Big Data
5.4 Features of Big Data to Be Integrated into the Smart Grid
5.5 Contribution of the Smart Grid as Data Source
5.6 Smart Grid in Supply of Data Gathering
5.6.1 Data Transmission Methodology
5.6.2 Data Analysis Methodology
5.6.3 Data Extraction from Smart Grid
5.6.4 Grid for Production of Renewable Source of Energy
5.6.5 Big Data in Smart Grid
5.6.6 Machine Learning Approach to the Data Grid
5.6.7 Application of IOT to the Smart Grid Technology
5.7 IOT Based Solutions Towards Grid Problems
5.7.1 Stability of IOT Based Connection
5.7.2 Cost Effectiveness in Implementation
5.7.3 Security to the Information
5.8 Application of Data Grid in Mobile Sink Based Wireless Sensor Network
5.8.1 Assumptions of Network Characteristics
5.9 Virtual Grid Architecture
5.9.1 Different Structures of Virtual Grids
5.9.2 Virtual Grid Construction Cost
5.9.3 Reading of the Smart Meter Data and Its Analysis by the Smart Grid with Future Prediction
5.9.4 Prediction Analysis of Smart Meter Data
5.10 Future Research Direction
5.11 Conclusion
References
6 Prediction and Classification for Smart Grid Applications
6.1 Introduction
6.2 Smart Grid
6.3 Predictive and Classification Models in Smart Grid Applications
6.4 Predictive Modeling
6.5 Classification Modeling
6.6 Smart Grid Management
6.7 Intelligent Data Collection Devices
6.8 Data Science Pertaining to Smart Grid Analytics
6.9 Machine Learning for Data Analytics
6.10 Data Security for Smart Grid Applications
6.11 Conclusion
References
7 A Review on Smart Metering Using Artificial Intelligence and Machine Learning Techniques: Challenges and Solutions
7.1 Introduction
7.1.1 Trends of the Smart Metering Systems
7.1.2 Challenges of Smart Meters
7.1.3 Key Elements of Smart Meter
7.1.4 IoT in Smart Metering
7.1.5 Integration of IoT with AI and Machine Learning for Smart Meter
7.1.6 Artificial Intelligence Techniques
7.2 Conclusion
References
8 Machine Learning Applications for the Smart Grid Infrastructure
8.1 Introduction
8.2 IoT in Distribution System
8.3 Techniques Using Machine Learning
8.4 Conclusion
References
9 A Privacy Mitigating Framework for the Smart Grid Internet of Things Data
9.1 Introduction
9.1.1 Overview of the Smart Grid and Its Significance in Modern Energy Systems
9.1.2 Introduction to the IoT and Its Integration with the Smart Grid
9.1.3 Importance of Privacy in Smart Grid IoT Data
9.2 Privacy Challenges in Smart Grid IoT Data
9.3 Privacy Mitigation Techniques
9.4 Privacy Mitigation Framework for Smart Grid
9.4.1 Privacy Monitoring Engine Description
9.5 Results
9.6 Conclusion
References
10 Protecting Future of Energy: Data Security and Privacy for Smart Grid Applications Using MATLAB
10.1 Introduction
10.1.1 Data Security and Privacy Threats
10.1.2 Data Security and Privacy Solutions
10.1.3 MATLAB Solution
10.1.4 Key Features and Capabilities
10.2 MATLAB Tools and Inbuilt Functions for Data Security in Applications of Smart Grid
10.3 MATLAB Functions for Data Security and Privacy in Smart Grid Applications Include
10.4 MATLAB Techniques for Data Security and Privacy in Smart Grid Applications
10.5 Matlab Algorithm for Privacy-Preserving Data Mining for Smart Grid Applications
10.6 Threats to Data Security and Privacy in Smart Grid Applications
10.6.1 Preventive Measures
10.7 Case Studies and Practical Implementations of Data Security and Privacy in Smart Grid Applications
10.7.1 Case Study 1: Securing Smart Meters Using Blockchain
10.7.2 Case Study 2: Machine Learning-Based Anomaly Detection in Power Grids
10.7.3 Case Study 3: Privacy-Preserving Data Aggregation in Smart Grids
10.7.4 Case Study 4: Secure Data Sharing in Smart Grids Using Homomorphic Encryption
10.7.5 Case Study 5: Anomaly Detection in Smart Grids Using Machine Learning (ML) with Matlab
10.8 Conclusion
References
11 Revolutionizing Smart Grids with Big Data Analytics: A Case Study on Integrating Renewable Energy and Predicting Faults
11.1 Introduction
11.2 Current Trends in Smart Grid Based Big Data Analytics
11.2.1 There is a Notable Surge in Speculation in Smart Grid Projects and, Consequently, Smart Grid Analytics [9–11]
11.2.2 Smart Grid Analytics Effectively Handle Real-Time Data Despite the Increased Speed and Diverse Requirements
11.2.3 Digital Technologies and Cloud Computing Will Continue to Improve, Facilitating Enhanced Data Computation Capabilities
11.2.4 Smart Grid and Its Benefits for Renewable Energy
11.3 Challenges of Smart Grid Analytics
11.3.1 Benefits of Analytics in Smart Grid
11.3.2 Trends in the Utility Industry
11.4 Technologies for Smart Grid Analytics and Its Importance
11.4.1 Business Intelligence (BI) and Data Analysis
11.4.2 Other Framework Technologies—Databases Such as Apache Hadoop, MapReduce, and SQL
11.4.3 The Significance of Big Data in Smart Grid Analytics
11.5 Gaining Perceptions Through a Smart Grid and Big Data: A Case Study
11.5.1 Case Studies in Focus
11.5.2 Smart Grid Based Data Analytics Use-Cases in Europe
11.6 Future and Scope of Big Data Analytics in Smart Grids
11.6.1 Customer Acceptance and Engagement
11.6.2 Regulatory Policies
11.6.3 Innovative Structures
11.7 Conclusion
References
12 Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques
12.1 Introduction
12.1.1 Statistics of Social Media Usage
12.1.2 Why Are Fake Profiles Created?
12.2 Literature Review
12.3 Proposed System for Detecting Fake Accounts on Twitter Using AI
12.3.1 Artificial Neural Network (ANN)
12.3.2 Support Vector Machine (SVM)
12.3.3 Random Forest (RF)
12.4 Findings and Discussions
12.5 Conclusion
References
13 Data Analytics for Smart Grids Applications to Improve Performance, Optimize Energy Consumption, and Gain Insights
13.1 Introduction
13.2 Leveraging Smart Grids for Predictive Energy Analytics
13.3 Big Data Analytics for Grid Resiliency and Security
13.4 Machine Learning Techniques for Smart Grid Optimization
13.5 Automated Demand Response for Smart Grid Efficiency
13.6 Applying Deep Learning for Demand Forecasting in Smart Grids
13.7 Integrating IoT Sensors with Smart Grids for Analyzing Grid Performance
13.8 Utilizing Blockchain Technology for Automating Smart Grid Transactions
13.9 Developing a Risk Assessment Model for Smart Grid Security
13.10 Leveraging AI for Automating Smart Grid Maintenance
13.11 The Role of Cloud Computing in Smart Grid Analytics
13.12 Conclusion
References
14 Advanced Digital Twin Technology: Opportunity and Challenges
14.1 Introduction
14.1.1 What is Digital Twins?
14.1.2 Advanced Digital Twin Technology
14.1.3 How Digital Twins Are Transforming Manufacturing
14.2 Benefits of Digital Twins in Manufacturing
14.2.1 Product Lifecycle in Digital Twin
14.3 Case Studies of Digital Twins in Manufacturing
14.4 Challenges and Limitations of Digital Twins in Manufacturing
14.5 Physical Object Versus Digital Twin
14.6 Future of Digital Twins in Manufacturing
14.6.1 IoT Used in Industry with Sensors and Using It for Further Automation
14.6.2 Virtual Vision for Finding Defects in machine’s
14.7 Several Opportunities of Digital Twin Technology
14.8 Conclusion
References
15 Machine Learning Applications for the Smart Grid
15.1 Introduction
15.2 Overview of Smart Grid
15.2.1 Smart Grid Functions
15.2.2 Benefits of Smart Grid
15.2.3 Self Healing Grid
15.2.4 Comprehensive Smart Grid
15.2.5 Smart Grid Technologies
15.3 Smart Meters
15.3.1 AMI Needs in the Smart Grid
15.4 Machine Learning Applications in Smart Grid
15.4.1 Neural Networks
15.4.2 Decision Trees
15.4.3 Support Vector Machines
15.4.4 Random Forests
15.4.5 Bayesian Networks
15.5 Conclusion
References
16 Intelligent Data Collection Devices in Smart Grid
16.1 Introduction
16.1.1 Necessity of Smart Grid
16.1.2 Electric Power Measurements in Three Phases
16.1.3 Achieving Precise 3-Phase Monitoring
16.1.4 DAQ Systems
16.1.5 Primary PC Based DAQ
16.2 Transducers (Sensors)
16.2.1 Conditional Signaling
16.2.2 Digital-to-Analog Converter
16.2.3 Computer with DAQ Software
16.3 Data Acquisition Types
16.3.1 Analogue DAQ
16.3.2 Digital DAQ
16.3.3 Stand-Alone DAQ
16.3.4 Process of Measurement in DAQ
16.3.5 Intelligent Electronic Devices (IED)
16.3.6 IED Block Diagram
16.3.7 Layout of Hardware and Software
16.3.8 Module for Communication
16.3.9 Advanced Metering Infrastructure (AMI)
16.4 Model for a Smart Grid Architecture (SGAM)
16.4.1 SGAM SG Aircraft
16.4.2 SGAM Interoperability Layers
16.5 Architecture with Three Layers
16.6 Conclusion
References
17 5G Multi-Carrier Modulation Techniques: Prototype Filters, Power Spectral Density, and Bit Error Rate Performance
17.1 Introduction
17.2 Candidate Waveforms System Model for 5G
17.2.1 Cyclic Prefix Orthogonal Frequency Division Multiplexing System Model
17.2.2 Filtered-OFDM (F-OFDM) System Model
17.2.3 Filter Bank Multi-Carrier (FBMC) System Model
17.2.4 Universal Filtered Multicarrier (UFMC) System Model
17.2.5 Generalized Frequency Division Multiplexing System Model
17.3 Results and Discussion
17.4 Conclusion
References
18 Towards Applications of Machine Learning Algorithms for Sustainable Systems and Precision Agriculture
18.1 Introduction
18.2 Background of Machine Learning Algorithms
18.2.1 Supervised Learning
18.2.2 Unsupervised Learning
18.2.3 Reinforcement Learning
18.2.4 Importance of Machine Learning
18.3 Application of Machine Learning in Agriculture
18.3.1 Problems in Agriculture
18.3.2 Crop Management
18.3.3 Water Management
18.3.4 Soil Management
18.3.5 Livestock Management
18.4 Recent Advances
18.5 Conclusion and Future Research Directions
References
19 Innovative Smart Grid Solutions for Fostering Data Security and Effective Privacy Preservation
19.1 Introduction
19.2 Data Security Challenges in Smart Grids
19.2.1 Data Integrity and Authentication
19.2.2 Data Confidentiality and Encryption
19.2.3 Access Control and Authorization
19.3 Smart Grids’ Privacy Preservation
19.3.1 Privacy Concerns in Smart Grids
19.3.2 Data Collection Techniques Concerning Privacy
19.3.3 Privacy-Preserving Data Sharing
19.4 Secure Communication in Smart Grids
19.4.1 Network Infrastructure Security
19.4.2 Secure Metering Infrastructure
19.5 Security Management and Incident Response
19.5.1 Security Policy Development
19.5.2 Security Monitoring and Incident Response
19.6 Case Studies: Data Security and Privacy Solutions
19.6.1 Secure Data Aggregation Techniques
19.6.2 Privacy-Preserving Demand Response
19.6.3 Related Case Studies
19.7 Threat Detection and Intrusion Prevention
19.7.1 Anomaly Detection Techniques
19.7.2 Intrusion Prevention Systems (IPS)
19.8 Secure Firmware and Software Updates
19.8.1 Secure Over-The-Air Updates
19.8.2 Secure Bootstrapping
19.9 Privacy-Preserving Data Analytics
19.9.1 Privacy-Preserving ML
19.9.2 Differential Privacy in Data Analytics
19.10 Blockchain for Data Security and Privacy
19.10.1 Blockchain Technology
19.10.2 Privacy-Enhancing Features
19.11 Conclusion and Future Directions
References
20 Unification of Internet of Video Things (IoVT) and Smart Grid Towards Emerging Information and Communication Technology (ICT) Systems
20.1 Introduction
20.2 IoVT’s Properties
20.2.1 Deployment of Large-Scale Vision Sensors Has Significantly Increased
20.2.2 Processing that is Strong and Economical in Terms of Energy
20.2.3 Via the Evolution of 5G and B5G, the Connection has Increased Rapidly
20.3 Edge Computing and “Cloud” Computing are Developing Quickly
20.3.1 Edge Computing
20.3.2 Cloud Computing
20.4 The IoVT's Technical Concerns
20.4.1 IoVT Smart Sensing Issues
20.4.2 IoVT Pervasive Networking Issues
20.4.3 IoVT Intelligent Integration Issues
20.5 IoVT Emerging Applications
20.5.1 Applications in Medicine and Healthcare
20.5.2 Applied to Mobile Devices
20.5.3 Applications for Automobiles and Traffic
20.5.4 Automation Applications
20.5.5 Industrial Manufacturing Applications
20.6 Conclusion
References
21 Human Face Recognition and Facial Attribute Analysis Using Data Analytics Techniques in Smart Grid Using Image Processing
21.1 Introduction
21.2 Literature Review
21.2.1 Deep Face Recognition
21.2.2 Attribute Classification
21.3 Proposed Methodology
21.4 Result Analysis and Discussion
21.5 Conclusion
References
22 Data Analytics Techniques for Smart Grids Applications Using Machine Learning
22.1 Introduction
22.2 Smart Grids Data Acquisition and Pre-Processing Techniques
22.2.1 Data Acquisition Techniques
22.2.2 Pre-Processing Techniques
22.3 Role of Smart Grid Data Mining
22.3.1 Role of Clustering, Classification, and Association Rule Mining in Smart Grid
22.4 Role of Machine Learning in Data Analytics in Smart Grid
22.4.1 Data Analytics in Smart Grid Using Support Vector Machines (SVMs)
22.4.2 Data Analytics in Smart Grid Using Random Forest (RF) Algorithm
22.4.3 Data Analytics in Smart Grid Using K-Nearest Neighbor (KNN)
22.5 Role of Data Analytics for Smart Grids Applications Using Deep Learning
22.5.1 Convolutional Neural Networks (CNN)
22.5.2 Recurrent Neural Networks (RNN)
22.5.3 Long Short-Term Memory (LSTM)
22.5.4 Generative Adversarial Networks (GAN)
22.6 Conclusion
References
23 Homorphic Encryption in Smart Grid System for Secure Information Aggregation
23.1 Introduction
23.2 Literature Review
23.3 Methodology
23.3.1 Homomorphic Cryptosystems
23.3.2 Paillier Cryptosystem
23.3.3 Homomorphic Properties
23.4 Result Analysis and Discussion
23.5 Conclusion
References