1st Edition

Internet of Energy for Smart Cities
Machine Learning Models and Techniques

  • Available for pre-order. Item will ship after July 20, 2021
ISBN 9780367497750
July 20, 2021 Forthcoming by CRC Press
328 Pages 112 B/W Illustrations

USD $150.00

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Book Description

Machine learning approaches has the capability to learn and adapt to the constantly evolving demands of large Internet-of-energy (IoE) network. The focus of this book is on using the machine learning approaches to present various solutions for IoE network in smart cities to solve various research gaps such as demand response management, resource management and effective utilization of the underlying ICT network. It provides in-depth knowledge to build the technical understanding for the reader to pursue various research problems in this field. Moreover, the example problems in smart cities and their solutions using machine learning are provided as relatable to the real-life scenarios. Aimed at Graduate Students, Researchers in Computer Science, Electrical Engineering, Telecommunication Engineering, Internet of Things, Machine Learning, Green computing, Smart Grid, this book:

  • Covers all aspects of Internet of Energy (IoE) and smart cities including research problems and solutions.
  • Points to the solutions provided by machine learning to optimize the grids within a smart city set-up.
  • Discusses relevant IoE design principles and architecture.
  • Helps to automate various services in smart cities for energy management.
  • Includes case studies to show the effectiveness of the discussed schemes.

Table of Contents

SECTION I Overview
Chapter 1 Smart City: The Verticals of Energy Demand and Challenges
Sumedha Sharma, Ashu Verma, and B.K. Panigrahi
1.1 Introduction
1.2 Smart Energy Distribution
1.2.1 Demand Response
1.2.2 Demand Side Management
1.3 Real-Time Grid Analytics and Data Management
1.3.1 Energy System Operations
1.3.2 Energy Management Systems
1.3.3 Design and Formulation of Optimizer Model
1.3.4 Real-Time Optimization Robust Optimization Stochastic Programming with Recourse Chance-Constrained Optimization
1.3.5 Big Data Analytics
1.3.6 Energy Blockchain
1.4 Intelligent Cloud based Grid Applications
1.4.1 Centralized Control
1.4.2 Decentralized Control
1.4.3 Distributed Control
1.4.4 Multi-Agent Systems
1.5 Conclusion

SECTION II Smart Grids
Chapter 2 Conventional Power Grid to Smart Grid
Dristi Datta and Nurul I Sarkar
2.1 Introduction
2.2 Evolution: From Power Grid to Smart Grid
2.3 Benefits of Smart Grid System
2.3.1 Technological Benefits
2.3.2 Benefits to Customers
2.3.3 Benefits to Stakeholders
2.4 Smart Grid: Standards and Technologies
2.4.1 Standards Revenue Metering Information Model Building Automation Substation Automation Powerline Networking Home Area Network Device Communication
Measurement and Control Application-Level Energy Management
Systems Inter-Control and Interoperability Center
Communications Cyber Security Electric Vehicles
2.4.2 Technologies Storage Systems Telecommunication Systems ICT Infrastructure
2.5 Implementation Aspects
2.6 Challenges of Implementing Smart grid
2.6.1 Technical Challenges
2.6.2 Socio-economic Challenges
2.6.3 Miscellaneous
2.7 Open Research Questions
2.8 Concluding Remarks

Chapter 3 Smart Grids: An Integrated Perspective
Rafael S. Salles, B. Isa´ıas Lima Fuly, and Paulo F. Ribeiro
3.1 Introduction
3.2 Design challenges and philosophical considerations
3.2.1 Challenges and Technical Barriers Renewable Generation Sources Management and Market Complexity Power Quality Issues Cybersecurity
3.2.2 Holistic Normative Engineering Design for
Smart Grids
3.3 Smart grid architectures and technologies
3.3.1 The Communication Structure and Technologies
3.3.2 Smart Metering, Measurements, Control and
3.3.3 Microgrids and Key Technologies
3.4 Interoperability and scalability
3.4.1 Moving for Interoperability in Smart Grids
3.4.2 Scalability Aspects for the Modern Grid Model
3.5 Applications
3.5.1 Distributed Energy Resources Management
3.5.2 Energy Storage
3.5.3 Metering and Automation

SECTION III Internet of Energy (IoE)
Chapter 4 Internet of Energy: Solution for Smart Cities
Ash Mohammad Abbas
4.1 Introduction
4.2 Constituents of Smart Cities
4.2.1 Participation of Citizen
4.2.2 Residential Buildings
4.2.3 Street Lights
4.3 Need of IoE in Smart Cities
4.4 Problems to be Solved Using IoE
4.5 Operation of IoE
4.6 Integration of Electrical Vehicles to IoE
4.7 Infrastructure Required for IoE
4.8 IoE Tools
4.9 Conclusion

Chapter 5 IoE Applications for Smart Cities
Manju Lata and Vikas Kumar
5.1 Introduction
5.2 Energy Challenges in IoE
5.2.1 Reliability and Scalability
5.2.2 Security and Privacy intended for Data Access
5.2.3 Cost and Expenditure
5.2.4 Climate Conditions
5.2.5 Legislation
5.2.6 Education and Engagement of Citizens
5.2.7 Infrastructure and Capacity Building
5.3 Resolutions to IoE Energy Challenges
5.4 Smart Applications of IoE
5.5 Conclusion

Chapter 6 IoE Design Principles and Architecture
Rania Salih Abdalla, Sara A. Mahbub, Rania A. Mokhtar, Elmustafa
Sayed Ali, and Rashid A. Saeed
6.1 Introduction
6.2 IoE Architecture Models
6.2.1 An EMS-Based Architecture
6.2.2 A Fog Based Architecture
6.3 Embedding Intelligence in IoE Design
6.3.1 Cloud Computing
6.3.2 Fog Computing
6.3.3 Blockchain
6.4 IoE Standards8
6.4.1 IEEE 2030 Standard
6.4.2 IEEE 802.15.4g
6.4.3 IEEE 21450 and IEEE 21451
6.4.4 The 4th G -Based Low Power Wide Area (LPWA)
6.5 IoE Interoperability
6.6 IoE Privacy and Security
6.6.1 IoE Cyber Security
6.6.2 IoE Hardware Security
6.7 Conclusion

SECTION IV Machine Learning Models
Chapter 7 Machine Learning Models for Smart Cities
Dristi Datta and Nurul I Sarkar
7.1 Introduction
7.2 Machine Learning Frameworks
7.2.1 Machine Learning Approaches
7.2.2 Machine Learning Models Supervised Learning Models Unsupervised Learning Models Semi-supervised Learning Models Reinforcement Learning Model
7.3 Problem-solving using Machine Learning Techniques
7.4 Smart City Design Infrastructure
7.5 Smart City Design Challenges
7.5.1 Technical Challenges
7.5.2 Social Challenges
7.5.3 Economic Challenges
7.5.4 Miscellaneous Challenges
7.6 Implications of ML Models in the Design of Smart Cities
7.7 Concluding Remarks

Chapter 8 Machine Learning Models in Smart Cities - Data-Driven Perspective
Seyed Mahdi Miraftabzadeh, Michela Longo, and Federica Foiadelli
8.1 Introduction
8.2 Artificial Intelligence and the smart cities
8.3 Machine Learning
8.3.1 Categories of machine learning techniques
8.3.2 Big Data and Machine learning
8.4 Data terminology
8.4.1 Data definitions
8.4.2 Data type
8.4.3 Dataset in machine learning
8.5 Machine learning model
8.5.1 Model performance analysis (Error)
8.5.2 Validation Set
8.5.3 Model performance’s evaluation metrics Classification metrics Regression metrics
8.5.4 Machine learning algorithms Classification algorithms Regression algorithms

SECTION V Case Studies and Future Directions
Chapter 9 Case Study - 1: Machine Learning Techniques for Monitoring of PV Panel
Haba Cristian-Gyozo
9.1 Introduction
9.2 Solar panel monitoring
9.3 Photovoltaic operation degradation
9.4 Preventing measures
9.5 Real time data acquisition and analytics
9.5.1 Data sources Local data acquisition systems Meteorological mini stations Astronomical data Cloud services Alerting systems
9.6 Machine learning techniques in PV panel operation
9.7 Case study of system for PV panel monitoring
9.7.1 Photovoltaic systems in Romania
9.7.2 Description of the photovoltaic system
9.7.3 Weather station prototype
9.7.4 Data sources Data from PV system Weather ministations Cloud services
9.7.5 ML Methodology Data collection Data Preprocessing Model selection Feature selection Training and Validation
9.8 Conclusions

Chapter 10 Case Study - 2: Intelligent Control System for Smart Environment
Chintan Bhatt, Riya Patel, Siddharth Patel, Hussain Sadikot,
Akrit Khanna, and Esha Shah
10.1 Introduction
10.2 Related Work
10.3 Methodology
10.3.1 Privileged Access
10.3.2 Manual control of Electrical Appliances
10.3.3 Automatic control of electrical appliances
10.4 Experimental Set-up and Results
10.5 Discussion and Conclusion
10.6 Limitations
10.7 Future Enhancements

Chapter 11 Pathway and Future of the IoE in Smart Cities
Sharda Tripathi and Swades De
11.1 Introduction
11.2 IoE Application case studies
11.2.1 Smart monitoring of civic infrastructure and
11.2.2 Smart wireless services
11.2.3 Advanced power metering
11.2.4 Smart grid monitoring
11.3 Roles of big data and context-specific learning in future
11.3.1 Roles and challenges of big data
11.3.2 Node- and network-level data-driven optimization
11.4 Role and challenges of smart grid in IoE energy sustainability
11.4.1 Energy sustainability
11.4.2 Stability and controllability of power grid
11.5 Concluding remarks

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Anish Jindal:

Dr. Anish Jindal is working as a Lecturer (Assistant Professor) in the School of Computer Science and Electronic Engineering (CSEE), University of Essex since Mar 2020. Prior to this, he worked as a senior research associate at the School of Computing & Communications, Lancaster University, UK from Oct. 2018 to Mar. 2020. He completed his Ph.D., M.Engg. and B. Tech. degrees in computer science engineering in 2018, 2014, and 2012, respectively. He is the recipient of the Outstanding Ph.D. Dissertation Award, 2019 from the IEEE Technical Committee on Scalable Computing (TCSC) and conferred with the IEEE Communication Society's Outstanding Young Researcher Award for Europe, Middle East, and Africa (EMEA) Region, 2019. He has also been a visiting researcher to OFFIS - Institute for Information Technology, Germany in 2019. His research interests are in the areas of smart cities, data analytics, artificial intelligence, cyber-physical systems, wireless networks, and security. Some of his research findings are published in top-cited journals such as IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Electronics, IEEE Transactions on Vehicular Technology, IEEE Communication Magazine, IEEE Network, Future Generation Computer Systems, and Computer Networks. In addition to it, his research works have also been presented in various conferences of repute such as IEEE ICC, IEEE Globecom, IEEE WiMob, IEEE PES General Meeting, ACM MobiHoc, etc. He has served as General co-chair, TPC co-chair, TPC member, Publicity chair and Session chair of various reputed conferences and workshops including IEEE ICC, IEEE WoWMoM, IEEE INFOCOM and IEEE GLOBECOM. He is also the guest editor of various journals including Software: Practice and Experience (Wiley), Neural Computing & Applications (Springer), Computer Communications (Elsevier), and Computers (MDPI). He has also delivered many invited talks and lectures in various international avenues. He is a member of the ACM, IEEE, and actively involved with various working groups and committees of IEEE and ACM related to smart grid, energy informatics and smart cities.




Neeraj Kumar:

Prof. Neeraj Kumar  (SMIEEE)  (2019, 2020 highly-cited researcher from WoS) is working as a Full Professor in the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala (Pb.), India.  He is also adjunct professor at Asia University, Taiwan, King Abdul Aziz University, Jeddah, Saudi Arabia. He has published more than 400 technical research papers (DBLP: https://dblp.org/pers/hd/k/Kumar_0001:Neeraj) in top-cited journals and conferences which are cited more than 14802 times from well-known researchers across the globe with current h-index of 65 (Google scholar: https://scholar.google.com/citations?hl=en&user=gL9gR-4AAAAJ). He is highly cited researcher in 2019, 2020 in the list released by Web of Science (WoS). He has guided many research scholars leading to Ph.D. and M.E./M.Tech. His research is supported by funding from various competitive agencies across the globe.  His broad research areas are Green computing and Network management, IoT, Big Data Analytics, Deep learning and cyber-security. He has also edited/authored 10 books with International/National Publishers like IET, Springer, Elsevier, CRC. Security and Privacy of Electronic Healthcare Records: Concepts, paradigms and solutions (ISBN-13: 978-1-78561-898-7), Machine Learning for cognitive IoT, CRC Press,  Blockchain, Big Data and Machine learning, CRC Press, Blockchain Technologies across industrial vertical, Elsevier, Multimedia Big Data Computing for IoT Applications: Concepts, Paradigms and Solutions (ISBN: 978-981-13-8759-3), Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019) (ISBN 978-981-15-3369-3). Probabilistic Data Structures for Blockchain based IoT Applications, CRC Press.  One of the edited text-book entitled, "Multimedia Big Data Computing for IoT Applications: Concepts, Paradigms, and Solutions" published in Springer in 2019 is having 3.5 million downloads till 06 June 2020. It attracts attention of the researchers across the globe. (https://www.springer.com/in/book/9789811387586). 

  He is serving as editors of ACM Computing Survey, IEEE Transactions on Sustainable Computing, IEEE Systems Journal, IEEE Network Magazine, IEEE Communication Magazine, Elsevier Journal of Networks and Computer Applications,  Elsevier Computer Communication,  Wiley International Journal of Communication Systems. Also, he has organized various special issues of journals of repute from IEEE, Elsevier, Springer.  He has been a workshop chair at IEEE Globecom 2018, IEEE Infocom 2020 (https://infocom2020.ieee-infocom.org/workshop-blockchain-secure-software-defined-networking-smart-communities)  and IEEE ICC 2020 (https://icc2020.ieee-icc.org/workshop/ws-06-secsdn-secure-and-dependable-software-defined-networking-sustainable-smart)  and track chair of Security and privacy of IEEE MSN 2020 (https://conference.cs.cityu.edu.hk/msn2020/cf-wkpaper.php). He is also TPC Chair and member for various International conferences such as IEEE MASS 2020, IEEE MSN2020. He has won the best papers award from IEEE Systems Journal and IEEE ICC 2018, Kansas-city in 2018. He won the best researcher award from parent organization every year from last eight consecutive years.


Gagangeet Singh Aujla:

Dr. Gagangeet Singh Aujla is an Assistant Professor of Computer Science at Durham University. Before this, he worked as a post-doctoral research associate at Newcastle University, a research associate at Thapar University (India), a visiting researcher at University of Klagenfurt (Austria) and on various academic positions for more than a decade. He received his PhD degree from the Thapar University (India), Master and Bachelor degrees from the Punjab Technical University (India). For his contributions to the area of scalable and sustainable computing, he was awarded the 2018 IEEE TCSC Outstanding PhD Dissertation Award of Excellence. The main theme of his research is energy-efficient, reconfigurable, resilient and intelligent surfaces (smart city, smart grid, IoT-Edge-Cloud systems, healthcare systems, transportation systems). To develop himself as a researcher, he worked on various research projects awarded by EPSRC, the Department of Science and Technology (India), and Austrian Federal Ministry of Education, Science and Research. This facilitated his collaboration with international researchers from the UK, US, Canada, Australia, China, Austria, Brazil, and India. Together, the group published several research papers in the fields of software-defined networking, energy-efficient cloud data centres, edge-cloud computing, blockchain, particularly focusing on the creation of reconfigurable, resilient and intelligent surfaces. He led his team organizing workshops (SecSDN and BlockSecSDN) in conjunction with different IEEE Communication Society conferences like IEEE Infocom, IEEE Globecom, IEEE ICC, and many more. Contributing to the research community, he is serving as Associate Editor for Ad-hoc Networks and Topic Editor for Sensors, a Guest Editor for IEEE Transaction on Industrial Informatics, IEEE Network, Neural Computing and Applications (Springer), Computer Communications (Elsevier), and Transactions on Emerging Telecommunications (Wiley).