Data Analytics for Smart Grids Applications―A Key to Smart City Development (Intelligent Systems Reference Library, 247)

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