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Cloud Computing in Smart Energy Meter Management

Edited by G. Senbagavalli, T. Kavitha, N. Amuthan, and Ferdin Joe John Joseph
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394193653  |  Hardcover  |  
530 pages
Price: $225 USD
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One Line Description
Cloud Computing in Smart Energy Meter Management equips you with essential insights and practical solutions for effectively managing smart meter data through cutting-edge technologies like artificial intelligence and cloud computing, making it an invaluable resource for anyone looking to enhance their understanding of modern energy management.

Audience
Undergraduate and postgraduate students and educators, policymakers, researchers, and industry professionals involved in electrical engineering, computer science, information science, and network and communication engineering

Description
Cloud Computing in Smart Energy Meter Management presents a structured review of the current research on smart energy meters with artificial intelligence and cloud computing solutions. This book will help provide solutions for processing and analyzing the massive amounts of data involved in smart meters through cloud computing. Readers will learn about data storage, processing, and dynamic pricing of smart energy data in the cloud, as well as smart metering concepts dealing with the flow of power consumption from consumer to utility center. It offers an in-depth explanation of advanced metering infrastructure (AMI) which includes meter installation, meter advising, commissioning, integration, master data synchronization, billing, customer interface, complaints, and resolution. In smart cities, components in household energy meters are fitted with sensors and can interconnect with the Internet of Things to measure power consumption with an automated meter reading. This book also acts as a new resource describing new technologies involved in the integration of smart metering with existing cellular networks. Cloud Computing in Smart Energy Meter Management provides knowledge on the vital role played by artificial intelligence and cloud computing in smart energy meter reading with precise evaluations.

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Author / Editor Details
G. Senbagavalli, PhD is an associate professor in the Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, India with over 18 years of experience in teaching and research. She has published three patents, two book chapters, and 15 papers in national and international conferences and journals. She is also a lifetime member of the International Society for Technology in Education and the Institution of Electronics and Telecommunications Engineers. Her research interests include image and video processing, computer vision, machine learning, and VLSI Design.

T. Kavitha, PhD is a professor in the Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, India with over twenty years of experience in teaching and research. She has published five patents, two book chapters, 15 papers in international journals, and over 30 papers in national and international conferences. She is also a lifetime member of the International Society for Technology in Education and the Institution of Engineers (India). Her research interests include wireless networks, wireless sensor networks, information security, Internet of Things, deep learning, and machine learning.

N. Amuthan, PhD is a professor at AMC Engineering College, Bengaluru, India with over 22 years of teaching experience. He has over 26 publications in reputed national and international conferences, workshops, and journals and serves as a reviewer for various national and international journals. He is also a member of numerous national and international committees and societies. His research interests include power electronics, energy conservation, auditing, renewable energy sources, and implementation of the cloud for integration at the national level.

Ferdin Joe John Joseph, PhD is an assistant professor in the Department of Information Technology at the Thai Nichi Institute of Technology, Bangkok with over a decade of teaching experience. He has several publications in international journals and conferences and has been designated as a Most Valuable Professional with Alibaba Cloud. His areas of research include deep learning, Internet of Things, and Cloud AI.

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Table of Contents
List of Contributors
Preface
1. Fundamentals of Smart Meter
G. Senbagavalli, T. Kavitha and S.T. Bibin Shalini
1.1 Introduction
1.2 Advanced Metering Infrastructure (AMI)
1.2.1 Foundational Elements of AMI
1.2.2 Benefits of AMI
1.2.3 Features of Smart Meters in AMI
1.3 Types of Smart Meters
1.3.1 Residential Smart Meter Types
1.3.2 Consumer-Based Smart Meter Types
1.4 Meter Standards
1.4.1 COSEM/DLMS
1.4.2 Reasons for Adopting COSEM in Various Nations, Consortiums, and Utilities
1.4.3 Types of Meters and the Corresponding Parameters
1.5 Testing and Maintenance of Smart Meters
1.6 AMI Data Management Services
1.6.1 AMI Data Requirements From Smart Meters
1.6.2 Meter Data Management Services
1.7 Demand Response
1.7.1 Automated Demand Response
1.8 Cloud Services
1.8.1 Examples of Cloud Services
1.9 Security in Smart Meters
1.10 Case Studies
1.10.1 Houston, TX—Centerpoint Energy
1.10.2 Maine—Central Maine Power (CMP)
1.10.3 Oklahoma and Western Arkansas—Oklahoma Gas and Electric (OG&E)
1.10.4 Washington, District of Columbia—Potomac Electric Power Company (PEPCO)
1.10.5 Chattanooga, TN—Electric Power Board (EPB)
1.10.6 Northern Florida—Talquin Electric Cooperative (TEC)
1.10.7 Oregon—Central Lincoln People’s Utility District
1.10.8 North Carolina—Tri-State Electric Membership Corporation
1.10.9 India
Conclusion
References
2. Empowering Consumers and Utilities for a Smarter Future: The Pivotal Role of Advanced Metering Infrastructure (AMI) in Smart Meter Technology
N. Amuthan, M. Sathya and Nisha C. Rani
2.1 Introduction
2.1.1 Background on Smart Energy Metering Infrastructure System
2.2 AMI Architecture
2.3 How AMI Works?
2.4 Architecture and Components of AMI
2.5 AMI Protocols—Standards and Initiatives
2.6 Home Area Network
2.7 Neighborhood Area Network (NAN)
2.8 Functions of Head End Systems
2.9 Meter Data Management
2.10 AMI System Design/MDAS/MDMS
2.11 Metering Head End Design
2.11.1 Communication Protocol and Selection Technology
2.11.2 Power Line Communication (PLC)
2.11.3 Cellular Networks
2.11.4 Radio Frequency (RF) Mesh
2.11.5 Narrowband IoT (NB-IoT)
2.11.6 Zigbee
2.11.7 Ethernet
2.11.8 SigFox
2.11.9 WiMax
2.11.10 Digital Subscriber Line (DSL)
2.11.11 Fiber Optic Communications
2.12 Conclusion
References
3. Demystifying Smart Meters: Powering the Next-Generation Grid
M. Marsaline Beno, N. Sivakumar and R. Saravanan
3.1 Introduction
3.2 Exploring the Emerging Functionalities of Smart Meters
3.3 Smart Metering Infrastructure
3.3.1 AMI Blockchain
3.3.2 Geographical Information System (GIS)
3.3.3 Customer Information System (CIS)
3.4 Communication Technology for Smart Metering Applications
3.4.1 Powering Mobile Communication: A Look at Cellular Technologies like GSM, GPRS, 3G–4G, and LTE
3.4.2 Integration of Smart Meters Into the Grid Infrastructure
3.4.3 Data Management and Privacy in Smart Metering
3.4.4 The Local Area Network, Sometimes Known as LAN
3.5 Regulatory Framework for Smart Meter Deployment
3.6 Benefits of Smart Meters in Grid Modernization
3.7 Hardware of Smart Meter
3.8 Smart Meters and Consumer Empowerment
3.9 Smart Meter Using Internet of Things Technology
3.9.1 Smart Meter with Integrated IoT Connectivity
3.10 A Meter Using Cloud and Edge Computing
3.11 Wide-Area Network for Smart Energy Meters
3.12 Smart Meter in Internet of Energy (IoE)
3.12.1 Energy Efficiency and Demand Response
3.12.2 Cost Savings and Billing Accuracy
3.12.3 Environmental Impact and Sustainability
3.13 Implementation Strategies for Smart Meters in IoE
3.13.1 Policy and Regulatory Framework
3.13.2 Infrastructure Requirements
3.13.3 Stakeholder Engagement and Consumer Awareness
3.13.4 Successful Implementations of Smart Meters in IoE
3.14 Future Prospects and Innovations in Smart Meter Technology
3.15 Conclusion
References
4. Communication and Networking in Advanced Metering
N. Palani Karthik, Behara Mohith and Vallidevi Krishnamurthy
4.1 Olden Days Electric Meter
4.2 Government Initiative for Smart Meter
4.3 Introduction: Networking and Communication
4.3.1 Advantages of Computer Networking
4.3.2 Challenges of Networking
4.4 IoT with Smart Meters
4.5 Connectivity of Smart Meters
4.5.1 How Do Smart Meters Communicate?
4.6 Electric Utility Commission Architecture
4.6.1 Wide Area Network (WAN)
4.6.2 Kubernetes
4.6.3 Use of Kubernetes in Smart Meter
4.7 Technology Selection in Advanced Metering Architecture
4.8 Case Study of Smart Meter Using RF
4.8.1 Advantages of RF
4.8.2 Disadvantages of RF
4.9 Why RF is Better Than Other Technologies Like 2G, 3G, and 4G
4.10 Concise Use of RF and WAN
4.11 Conclusion
References
5. Meter Data Acquisition Using Cloud Computing
S. P. Angelin Claret and B. Prashanthi
5.1 Introduction
5.2 Literature Review
5.3 Methodology and Implementation of Smart Meters Using Cloud Platform
5.4 Machine Learning Algorithms for Advanced Metering
5.4.1 Linear Regression
5.4.2 Random Forest
5.4.3 K-Nearest Neighbors
5.4.4 Neural Networks
5.4.5 Support Vector Machines
5.5 Applications of Cloud Data Acquisition for Smart Meters
5.6 Implementing OSS Layer for Smart Meters
5.7 Challenges and Opportunities of Smart Metering with Cloud-Based Data Acquisition
5.8 Future Directions of Smart Metering with Cloud-Based Data Acquisition
5.8.1 Smart Data Acquisition Using Machine Learning
5.8.2 Smart Data Acquisition Using Deep Learning
5.8.3 Combined Approaches of CNN and RNN
5.8.3.1 Image Data Acquisition with CNNs
5.8.3.2 Sequential Data Acquisition with RNNs
5.9 Conclusion and Summary of Key Findings
References
6. Smart Energy Meter Data Management in the Cloud Hadoop, SQL, HBase
B. Priya Esther, Priya Boopalan and P. Velrajkumar
6.1 Introduction to Data Management
6.2 Benefits of Data Management
6.2.1 Scalability and Flexibility
6.2.2 Cost Savings and Efficiency
6.2.3 Decision-Making Improvement
6.2.4 Productivity Increase
6.2.5 Real-Time Data Analysis and Insights
6.3 Significant Benefits of Smart Energy Meter Data Management
6.4 Challenges of Data Management
6.4.1 Data Security and Privacy Issues
6.4.2 Data Integration and Interoperability
6.4.3 Reliability and Availability
6.5 Solutions and Strategies for Effective SEM Cloud Data Management
6.5.1 Implementing Robust Security Measures
6.5.2 Standardizing Data Formats and Protocols
6.5.3 Utilizing Backup and Redundancy Systems
6.6 Challenges in Data Management for Smart Energy Meter
6.6.1 The Volume of Data
6.6.2 Possibilities
6.7 Importance of Data Management for Smart Energy Meter
6.8 Data Management for Smart Energy Meter Architecture
6.9 Role of Cloud Computing in Data Management for Smart Energy Meter
6.10 Data Management for Smart Energy Meter in the Cloud
6.11 Smart Energy Meter Data Management Using Hadoop
6.11.1 Demand–Response
6.12 Storing and Accessing Smart Energy Meter Data Using SQL Databases
6.13 Storing and Accessing Smart Energy Meter Data Using HBase 1
6.14 Modern Technology for a Modern Grid
6.15 Benefits of Using a Managed Service in the Cloud
6.16 Capabilities of the Highest Order in Data Analytics and Machine Learning
6.17 Case Studies of Successful SEM Cloud Data Management
6.17.1 Example of a Utility Company Using the Cloud for Data Management
6.17.2 Benefits and Outcomes Achieved
6.18 Future Trends and Advancements in SEM Cloud Data Management
6.18.1 Internet of Things (IoT) Integration
6.18.2 Artificial Intelligence for Predictive Analytics
6.18.3 Enhanced Data Visualization and User Interfaces
Conclusion
References
7. Smart Energy Meter Data Processing and Billing
S. Jeyadevi and Kalyani
7.1 Billing System
7.1.1 Introduction to Electricity Meters
7.1.2 Conventional Electricity Billing System
7.1.3 Types of Tariffs in Electricity
7.1.4 Smart Meter Billing System
7.1.5 Benefits to Customers in Smart Meter Billing System
7.1.6 Advanced Features in Billing System
7.1.7 IoT-Based Smart Meter Billing System
7.2 Big Data Analytics in Smart Metering
7.2.1 Introduction to Big Data Analytics
7.2.2 Data Analytics Requirements in Smart Meters
7.2.2.1 Data Sources
7.2.2.2 Data Collection
7.2.2.3 Data Storage Techniques
7.2.2.4 Data Communication Techniques
7.2.2.5 Data Analysis Techniques
7.3 Data Flow From Smart Meter to Billing System
7.3.1 Smart Meter Data Management System
7.3.2 Automation of Meter Reading, Monitoring, and Billing Using AWS
7.4 Security in Smart Metering System
7.4.1 Security Threats in Smart Metering
7.4.2 Surveys and Challenges
7.4.3 Security Solutions in Smart Meter Framework
7.5 Integrating Legacy Metering Infrastructure Into Smart Metering Systems
7.5.1 Integration Requirements
7.5.2 Challenges in Integration and Possible Solutions
7.6 Conclusion and Future Scope
References
8. Smart Meter Security—Fraud Detection in Power Theft
B. Devi Vighneswari and Kothai Andal C.
8.1 Introduction
8.2 Different Aspects of Smart Meter Security
8.3 Data Privacy and Encryption
8.4 Authentication and Authorization
8.5 Firmware and Software Updates
8.6 Physical Security
8.7 Network Security
8.8 Remote Access Control
8.9 Device Identity Management
8.10 Anomaly Detection
8.11 Regulatory Compliance
8.12 User Understanding and Directions
Conclusion
References
9. Cybersecurity in ICT-Enabled Smart Metering Systems: Addressing Challenges and Implementing Solutions
J. Selvin Paul Peter, C. Rajesh Babu and B. Priya Esther
9.1 Introduction
9.2 Cyber Attack in Smart Meters
9.3 Blockchain in Smart Meters
9.3.1 Secure Energy Trading Framework with Blockchain and Smart Contracts
9.3.2 Smart Contract for Energy Trading
9.3.3 Blockchain Implementation and Performance
9.3.4 Performance
9.4 IoT-Enabled Smart Meters
9.4.1 IoT-Enabled Smart Meters in Home Appliances
9.4.2 Smart Meters: Transforming Energy Measurement
9.4.3 Tailoring Communication Protocols for Smart Meters
9.4.4 Scalability and Quality of Service in Smart Meter Networks
9.5 Navigating the Complex Landscape of Smart Grid Communications
9.6 Securing Smart Meters
9.6.1 Fortifying Smart Meters Against Intrusions
9.6.2 Advanced Techniques for Threat Detection
9.6.3 Proactive Measures for Cyber Resilience
9.6.4 Securing Smart Meter Architecture
9.6.5 Enhancing DNP3 Security Features
9.6.6 Bandwidth Challenges and Deployment Costs
9.6.7 Communication Network Considerations: Range and Security
9.6.8 Role of Data Encryption
9.6.9 Metering Infrastructure Technologies
9.6.10 Intrusion Detection for Data Integrity
9.6.11 Comprehensive Cybersecurity Strategy for Smart Metering
9.7 Conclusion
References
10. Challenges in Smart Metering
R. Selvamathi, V. Indragandhi and N. Amuthan
10.1 Introduction
10.2 Growth of Smart Meter
10.2.1 Electro-Mechanical Meters
10.2.2 Electronic Digital Meter
10.2.3 Smart Meters
10.2.4 Smart Grid
10.2.5 Why Smart Meters
10.2.6 Smart Metering
10.3 Challenges in the Replacement of Existing Meters with Smart Meters with Prepayment
10.3.1 Lack of Knowledge
10.3.2 Insufficient Grid Infrastructure
10.3.3 Internet Safety and Data Privacy
10.3.4 Fiscal Difficulty
10.4 Technology Challenges in Smart Metering
10.4.1 Traffic Engineering
10.4.2 Control of Firmware
10.4.3 Cyber Security
10.4.4 A Look at Some Scaling Up Considerations
10.5 Operational Challenges
10.6 Case Study
References
11. Quality of Service (QoS) Protocol in Advanced Metering Infrastructure (AMI)
Robin Rohit Vincent, Nisha F. and Rose Priyanka
11.1 Introduction to QoS in AMI
11.2 Background
11.2.1 AMI System
11.2.2 Benefits of QoS in AMI
11.2.3 QoS Features and Components
11.3 Smart Grid System
11.4 Proposed Research Contribution
11.5 Survey Related to QoS of AMI With Smart Grid
11.5.1 Smart Grid
11.5.2 Cloud Computing
11.5.3 QoS
11.5.4 IoT
11.6 Proposed Deep Learning-Based Optimization Model
11.6.1 Link Quality
11.6.2 Delay Model
11.6.2.1 Obtaining Dependability on the Path
11.6.2.2 Problem Formulation
11.7 Modeling a System and Formulating a Problem
11.7.1 DAP Localization Algorithm
11.7.1.1 Phase 1: Selection of Poles
11.7.1.2 Construction of Trees
11.8 Strategy Performed Along With Terms of Effectiveness as Well as Quick Confluence
11.8.1 Analysis of Optimality
11.8.2 Convergence Analysis
11.8.3 Complexity Analysis
11.8.4 Shortest Path
11.9 Results, Discussion, Findings, and Analysis
11.10 Conclusion
References
12. Web Services/Mobile Application to Monitor the Smart Meter Data
Jarin T., Muniraj Rathinam, Ulaganathan M., Aswin V. M. and Jithin K. Jose
12.1 Introduction
12.1.1 Kilowatt-Hour Meter
12.1.2 Smart Meter
12.1.3 Distortion-Based Approach
12.1.4 Automatic Meter Reading Applications
12.1.5 Hierarchical Framework
12.1.6 Ensemble Detection Model
12.1.7 Big Data-Driven Detection
12.1.8 Management of Smart Meters
12.2 Comparison of Kilowatt-Hour Meter and Smart Meter
12.2.1 Energy Management
12.2.2 Cost-Effectiveness
12.3 Mobile Applications for Smart Meter Data
12.3.1 Necessity of Mobile Apps
12.3.2 Working
12.3.3 Software Used
12.3.4 Mobile Applications Used
12.4 Comparison of Different Factors
12.4.1 Complexity of Usage
12.4.2 Cost
12.5 Conclusion
12.6 Future Scope
References
13. Advanced Smart Prepaid Meter
Ezhilarasi P., Ramesh L., Balamurugan J. and J.B. Holm-Nielsen
13.1 Introduction
13.1.1 Types of Smart Meter
13.1.1.1 Net Meters
13.1.1.2 Post-Paid Meters
13.1.1.3 Prepaid Meter
13.1.2 Prepaid Smart Energy Meter
13.1.2.1 Prepaid Meter Design
13.2 Literature Review
13.3 Cost-Efficient Futuristic M2M Smart Prepaid Meter
13.3.1 Advanced Smart Prepaid Meter (NESEM: A Single Meter with Multitude Benefits)
13.3.1.1 Add-On Device
13.3.1.2 NESEM
13.3.2 NESEM Hardware Design and Its Metering Algorithm
13.3.2.1 Hardware Design
13.3.2.2 Smart Prepaid Metering Algorithm
13.3.2.3 AI Algorithm
13.3.2.4 Security Algorithm
13.4 Smart Metering Results
13.4.1 Case Studies
13.4.1.1 Test Bed
13.4.1.2 Field Test
13.4.2 NESEM Functional Results
13.4.2.1 Remote Monitoring
13.4.2.2 Consumer Notification
13.4.2.3 Remote Connect/Disconnect
13.4.2.4 AI Outcomes
13.5 Conclusions with Future Research Scopes
References
14. Edge Computing and Cyber-Physical System in Smart Meter
Revathi M., Udayakumar K. and Prabhakaran M. V.
14.1 Introduction
14.2 Literature Survey
14.3 Smart Meter Components and Their Architecture
14.4 Smart Meter Data Analytics on Edge Devices
14.5 Smart Metering Infrastructure
14.6 IoT-Enabled Smart Meter
14.7 An Overview of Cyber-Physical System
14.7.1 CPS in an Energy Management System
14.7.2 Key Component of CPS
14.7.3 Benefits of CPS in Smart Grids
14.8 Case Study and Application
14.8.1 Energy Monitoring System
14.8.2 Transmission Line Monitoring
14.8.3 Energy Consumption Prediction
14.8.4 Outage Management
14.8.5 Smart Meter Device Malfunction Detection
14.8.6 Applications of Smart Metering
14.9 Challenges and Future Research Scopes
14.9.1 Smart Energy Meter Issues
14.9.2 Challenges in Edge Computing
14.9.3 Edge and Cloud Comparison
14.9.4 Open Research Challenges
14.10 Conclusion
References
15. Case Study on Real-Time Smart Meter
Yasha Jyothi M. Shirur, Bindu S. and Jyoti R. Munavalli
15.1 Introduction
15.2 Literature Review
15.3 Case Study 1: Smart Energy Monitoring
15.3.1 Methodology Adopted
15.3.2 Prototype Implementation and Results
15.3.3 Results and Discussion
15.4 Case Study 2: Power Theft
15.4.1 Methodology Adopted
15.4.2 Block Diagram
15.4.3 Working Principle
15.4.4 Results and Discussion
15.5 Conclusion
Acknowledgments
References
About the Editors
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