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.
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.
List of Contributors xvii
Preface xxiii
1 Fundamentals of Smart Meter 1
G. Senbagavalli, T. Kavitha and S.T. Bibin Shalini
1.1 Introduction 1
1.2 Advanced Metering Infrastructure (AMI) 3
1.3 Types of Smart Meters 6
1.4 Meter Standards 8
1.5 Testing and Maintenance of Smart Meters 11
1.6 AMI Data Management Services 12
1.7 Demand Response 14
1.8 Cloud Services 16
1.9 Security in Smart Meters 20
1.10 Case Studies 21
Conclusion 27
References 28
2 Empowering Consumers and Utilities for a Smarter Future: The Pivotal Role
of Advanced Metering Infrastructure (AMI) in Smart Meter Technology 31
N. Amuthan, M. Sathya and Nisha C. Rani
2.1 Introduction 32
2.2 AMI Architecture 36
2.3 How AMI Works? 39
2.4 Architecture and Components of AMI 41
2.5 AMI ProtocolsStandards and Initiatives 45
2.6 Home Area Network 47
2.7 Neighborhood Area Network (NAN) 52
2.8 Functions of Head End Systems 55
2.9 Meter Data Management 56
2.10 AMI System Design/MDAS/MDMS 56
2.11 Metering Head End Design 57
2.12 Conclusion 62
References 63
3 Demystifying Smart Meters: Powering the Next-Generation Grid 67
M. Marsaline Beno, N. Sivakumar and R. Saravanan
3.1 Introduction 67
3.2 Exploring the Emerging Functionalities of Smart Meters 69
3.3 Smart Metering Infrastructure 71
3.4 Communication Technology for Smart Metering Applications 76
3.5 Regulatory Framework for Smart Meter Deployment 79
3.6 Benefits of Smart Meters in Grid Modernization 80
3.7 Hardware of Smart Meter 82
3.8 Smart Meters and Consumer Empowerment 85
3.9 Smart Meter Using Internet of Things Technology 85
3.10 A Meter Using Cloud and Edge Computing 87
3.11 Wide-Area Network for Smart Energy Meters 88
3.12 Smart Meter in Internet of Energy (IoE) 89
3.13 Implementation Strategies for Smart Meters in IoE 90
3.14 Future Prospects and Innovations in Smart Meter Technology 92
3.15 Conclusion 93
References 95
4 Communication and Networking in Advanced Metering 99
N. Palani Karthik, Behara Mohith and Vallidevi Krishnamurthy
4.1 Olden Days Electric Meter 100
4.2 Government Initiative for Smart Meter 101
4.3 Introduction: Networking and Communication 104
4.4 IoT with Smart Meters 107
4.5 Connectivity of Smart Meters 108
4.6 Electric Utility Commission Architecture 111
4.7 Technology Selection in Advanced Metering Architecture 119
4.8 Case Study of Smart Meter Using RF 122
4.9 Why RF is Better Than Other Technologies Like 2G, 3G, and 4G 127
4.10 Concise Use of RF and WAN 129
4.11 Conclusion 131
References 133
5 Meter Data Acquisition Using Cloud Computing 137
S. P. Angelin Claret and B. Prashanthi
5.1 Introduction 138
5.2 Literature Review 139
5.3 Methodology and Implementation of Smart Meters Using Cloud Platform 142
5.4 Machine Learning Algorithms for Advanced Metering 146
5.5 Applications of Cloud Data Acquisition for Smart Meters 150
5.6 Implementing OSS Layer for Smart Meters 152
5.7 Challenges and Opportunities of Smart Metering with Cloud-Based Data
Acquisition 155
5.8 Future Directions of Smart Metering with Cloud-Based Data Acquisition
159
5.9 Conclusion and Summary of Key Findings 164
References 166
6 Smart Energy Meter Data Management in the Cloud Hadoop, SQL, HBase 169
B. Priya Esther, Priya Boopalan and P. Velrajkumar
6.1 Introduction to Data Management 170
6.2 Benefits of Data Management 174
6.3 Significant Benefits of Smart Energy Meter Data Management 177
6.4 Challenges of Data Management 178
6.5 Solutions and Strategies for Effective SEM Cloud Data Management 182
6.6 Challenges in Data Management for Smart Energy Meter 185
6.7 Importance of Data Management for Smart Energy Meter 186
6.8 Data Management for Smart Energy Meter Architecture 187
6.9 Role of Cloud Computing in Data Management for Smart Energy Meter 187
6.10 Data Management for Smart Energy Meter in the Cloud 188
6.11 Smart Energy Meter Data Management Using Hadoop 189
6.12 Storing and Accessing Smart Energy Meter Data Using SQL Databases 191
6.13 Storing and Accessing Smart Energy Meter Data Using HBase 192
6.14 Modern Technology for a Modern Grid 193
6.15 Benefits of Using a Managed Service in the Cloud 194
6.16 Capabilities of the Highest Order in Data Analytics and Machine
Learning 195
6.17 Case Studies of Successful SEM Cloud Data Management 198
6.18 Future Trends and Advancements in SEM Cloud Data Management 200
Conclusion 202
References 204
7 Smart Energy Meter Data Processing and Billing 207
S. Jeyadevi and Kalyani
7.1 Billing System 208
7.2 Big Data Analytics in Smart Metering 218
7.3 Data Flow From Smart Meter to Billing System 224
7.4 Security in Smart Metering System 228
7.5 Integrating Legacy Metering Infrastructure Into Smart Metering Systems
233
7.6 Conclusion and Future Scope 236
References 236
8 Smart Meter SecurityFraud Detection in Power Theft 239
B. Devi Vighneswari and Kothai Andal C.
8.1 Introduction 240
8.2 Different Aspects of Smart Meter Security 241
8.3 Data Privacy and Encryption 243
8.4 Authentication and Authorization 245
8.5 Firmware and Software Updates 247
8.6 Physical Security 248
8.7 Network Security 250
8.8 Remote Access Control 252
8.9 Device Identity Management 254
8.10 Anomaly Detection 255
8.11 Regulatory Compliance 257
8.12 User Understanding and Directions 259
8.13 Conclusion 260
References 261
9 Cybersecurity in ICT-Enabled Smart Metering Systems: Addressing Challenges
and Implementing Solutions 263
J. Selvin Paul Peter, C. Rajesh Babu and B. Priya Esther
9.1 Introduction 264
9.2 Cyber Attack in Smart Meters 265
9.3 Blockchain in Smart Meters 266
9.4 IoT-Enabled Smart Meters 273
9.5 Navigating the Complex Landscape of Smart Grid Communications 280
9.6 Securing Smart Meters 283
9.7 Conclusion 287
References 288
10 Challenges in Smart Metering 291
R. Selvamathi, V. Indragandhi and N. Amuthan
10.1 Introduction 292
10.2 Growth of Smart Meter 294
10.3 Challenges in the Replacement of Existing Meters with Smart Meters with
Prepayment 300
10.4 Technology Challenges in Smart Metering 306
10.5 Operational Challenges 313
10.6 Case Study 314
References 315
11 Quality of Service (QoS) Protocol in Advanced Metering Infrastructure
(AMI) 319
Robin Rohit Vincent, Nisha F. and Rose Priyanka
11.1 Introduction to QoS in AMI 320
11.2 Background 321
11.3 Smart Grid System 324
11.4 Proposed Research Contribution 325
11.5 Survey Related to QoS of AMI With Smart Grid 326
11.6 Proposed Deep Learning-Based Optimization Model 327
11.7 Modeling a System and Formulating a Problem 338
11.8 Strategy Performed Along with Terms of Effectiveness as Well as Quick
Confluence 342
11.9 Results, Discussion, Findings, and Analysis 343
11.10 Conclusion 346
References 346
12 Web Services/Mobile Application to Monitor the Smart Meter Data 349
Jarin T., Muniraj Rathinam, Ulaganathan M., Aswin V. M. and Jithin K. Jose
12.1 Introduction 349
12.2 Comparison of Kilowatt-Hour Meter and Smart Meter 359
12.3 Mobile Applications for Smart Meter Data 362
12.4 Comparison of Different Factors 365
12.5 Conclusion 367
12.6 Future Scope 368
References 368
13 Advanced Smart Prepaid Meter 371
Ezhilarasi P., Ramesh L., Balamurugan J. and J.B. Holm-Nielsen
13.1 Introduction 372
13.2 Literature Review 379
13.3 Cost-Efficient Futuristic M2M Smart Prepaid Meter 386
13.4 Smart Metering Results 407
13.5 Conclusions with Future Research Scopes 415
References 416
14 Edge Computing and Cyber-Physical System in Smart Meter 419
Revathi M., Udayakumar K. and Prabhakaran M. V.
14.1 Introduction 420
14.2 Literature Survey 422
14.3 Smart Meter Components and Their Architecture 424
14.4 Smart Meter Data Analytics on Edge Devices 427
14.5 Smart Metering Infrastructure 428
14.6 IoT-Enabled Smart Meter 432
14.7 An Overview of Cyber-Physical System 434
14.8 Case Study and Application 438
14.9 Challenges and Future Research Scopes 442
14.10 Conclusion 448
References 449
15 Case Study on Real-Time Smart Meter 453
Yasha Jyothi M. Shirur, Bindu S. and Jyoti R. Munavalli
15.1 Introduction 454
15.2 Literature Review 458
15.3 Case Study 1: Smart Energy Monitoring 461
15.4 Case Study 2: Power Theft 468
15.5 Conclusion 482
Acknowledgments 483
References 484
About the Editors 487
Index 489
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.
