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Internet of Energy for Smart Cities: Machine Learning Models and Techniques [Hardback]

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  • Formāts: Hardback, 302 pages, height x width: 234x156 mm, weight: 580 g, 22 Tables, black and white; 112 Line drawings, black and white; 112 Illustrations, black and white
  • Izdošanas datums: 20-Jul-2021
  • Izdevniecība: CRC Press
  • ISBN-10: 0367497751
  • ISBN-13: 9780367497750
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Internet of Energy for Smart Cities: Machine Learning Models and TechniquesPalielināt
  • Formāts: Hardback, 302 pages, height x width: 234x156 mm, weight: 580 g, 22 Tables, black and white; 112 Line drawings, black and white; 112 Illustrations, black and white
  • Izdošanas datums: 20-Jul-2021
  • Izdevniecība: CRC Press
  • ISBN-10: 0367497751
  • ISBN-13: 9780367497750
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367497750.html
Keywords:

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.



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 helps build the technical understanding for the reader.
Preface xiii
Contributors xix
Section I Overview
Chapter 1 Smart City: The Verticals of Energy Demand and Challenges
3(34)
Sumedha Sharma
Ashu Verma
B.K. Panigrahi
1.1 Introduction
4(4)
1.2 Smart Energy Distribution
8(5)
1.2.1 Demand Response
10(2)
1.2.2 Demand Side Management
12(1)
1.3 Real-Time Grid Analytics and Data Management
13(14)
1.3.1 Energy System Operations
13(2)
1.3.2 Energy Management Systems
15(1)
1.3.3 Design and Formulation of Optimizer Model
16(2)
1.3.4 Real-Time Optimization
18(1)
1.3.4.1 Robust Optimization
19(1)
1.3.4.2 Stochastic Programming with Recourse
21(1)
1.3.4.3 Chance-Constrained Optimization
22(1)
1.3.5 Big Data Analytics
23(3)
1.3.6 Energy Blockchain
26(1)
1.4 Intelligent Cloud-Based Grid Applications
27(3)
1.4.1 Centralized Control
27(1)
1.4.2 Decentralized Control
28(1)
1.4.3 Distributed Control
28(2)
1.4.4 Multi-Agent Systems
30(1)
1.5 Conclusion
30(7)
Section II Smart Grids
Chapter 2 Conventional Power Grid to Smart Grid
37(32)
Dristi Datta
Nurul I. Sarkar
2.1 Introduction
38(2)
2.2 Evolution: From Power Grid to Smart Grid
40(5)
2.3 Benefits of Smart Grid System
45(2)
2.3.1 Technological Benefits
45(1)
2.3.2 Benefits to Customers
46(1)
2.3.3 Benefits to Stakeholders
46(1)
2.4 Smart Grid: Standards and Technologies
47(6)
2.4.1 Standards
47(1)
2.4.1.1 Revenue Metering Information Model
48(1)
2.4.1.2 Building Automation
49(1)
2.4.1.3 Substation Automation
49(1)
2.4.1.4 Powerline Networking
49(1)
2.4.1.5 Home Area Network Device Com- munication Measurement and Control
50(1)
2.4.1.6 Application-Level Energy Management Systems
50(1)
2.4.1.7 Inter-Control and Interoperability Center Communications
50(1)
2.4.1.8 Cyber Security
51(1)
2.4.1.9 Electric Vehicles
51(1)
2.4.2 Technologies
51(1)
2.4.2.1 Storage Systems
51(1)
2.4.2.2 Telecommunication Systems
52(1)
2.4.2.3 ICT Infrastructure
53(1)
2.5 Implementation Aspects
53(5)
2.6 Challenges of Implementing Smart Grid
58(1)
2.6.1 Technical Challenges
58(2)
2.6.2 Socio-Economic Challenges
60(2)
2.6.3 Miscellaneous
62(1)
2.7 Open Research Questions
63(1)
2.8 Concluding Remarks
64(5)
Chapter 3 Smart Grids: An Integrated Perspective
69(36)
Rafael S. Salles
B. Isaias Lima Fuly
Paulo F. Ribeiro
3.1 Introduction
70(2)
3.2 Design Challenges and Philosophical Considerations
72(9)
3.2.1 Challenges and Technical Barriers
72(1)
3.2.1.1 Renewable Generation Sources
72(1)
3.2.1.2 Management and Market Complexity
74(1)
3.2.1.3 Power Quality Issues
75(1)
3.2.1.4 Cybersecurity
78(1)
3.2.2 Holistic Normative Engineering Design for Smart Grids
79(2)
3.3 Smart Grid Architectures and Technologies
81(9)
3.3.1 The Communication Structure and Technologies
83(1)
3.3.2 Smart Metering, Measurements, Control, and Automation
84(3)
3.3.3 Microgrids and Key Technologies
87(3)
3.4 Interoperability and Scalability
90(4)
3.4.1 Moving for Interoperability in Smart Grids
90(1)
3.4.2 Scalability Aspects for the Modern Grid Model
91(3)
3.5 Applications
94(3)
3.5.1 Distributed Energy Resources Management
94(1)
3.5.2 Energy Storage
95(1)
3.5.3 Metering and Automation
96(1)
3.6 Summary
97(8)
Section III Internet of Energy (loE)
Chapter 4 IoE: Solution for Smart Cities
105(22)
Ash Mohammad Abbas
4.1 Introduction
105(3)
4.2 Constituents of Smart Cities
108(3)
4.2.1 Participation of Citizen
109(1)
4.2.2 Residential Buildings
110(1)
4.2.3 Street Lights
111(1)
4.3 Need of IoE in Smart Cities
111(1)
4.4 Problems to be Solved Using IoE
112(1)
4.5 Operation of IoE
113(3)
4.6 Integration of Electrical Vehicles to IoE
116(1)
4.7 Infrastructure Required for IoE
117(1)
4.8 IoE Tools
118(1)
4.9 Conclusion
119(8)
Chapter 5 IoE Applications for Smart Cities
127(18)
Manju Lata
Vikas Kumar
5.1 Introduction
127(2)
5.2 Energy Challenges in IoE
129(4)
5.2.1 Reliability and Scalability
129(1)
5.2.2 Security and Privacy Intended for Data Access
130(1)
5.2.3 Cost and Expenditure
130(1)
5.2.4 Climate Conditions
131(1)
5.2.5 Legislation
131(1)
5.2.6 Education and Engagement of Citizens
132(1)
5.2.7 Infrastructure and Capacity Building
132(1)
5.3 Resolutions to IoE Energy Challenges
133(1)
5.4 Smart Applications of IoE
134(5)
5.5 Conclusion
139(6)
Chapter 6 IoE Design Principles and Architecture
145(28)
Rania Salih Abdalla
Sara A. Mahbub
Rania A. Mokhtar
Elmustafa Sayed Ali
Rashid A. Saeed
6.1 Introduction
146(1)
6.2 IoE Architecture Models
147(7)
6.2.1 An EMS-Based Architecture
148(2)
6.2.2 A Fog-Based Architecture
150(4)
6.3 Embedding Intelligence in IoE Design
154(4)
6.3.1 Cloud Computing
154(2)
6.3.2 Fog Computing
156(1)
6.3.3 Blockchain
157(1)
6.4 IoE Standards
158(4)
6.4.1 IEEE 2030 Standard
159(1)
6.4.2 IEEE 802.15.4g
159(1)
6.4.3 IEEE 21450 and IEEE 21451
159(2)
6.4.4 The 4th G-Based Low Power Wide Area (LPWA)
161(1)
6.5 IoE Interoperability
162(1)
6.6 IoE Privacy and Security
162(4)
6.6.1 IoE Cyber Security
163(2)
6.6.2 IoE Hardware Security
165(1)
6.7 Conclusion
166(7)
Section IV Machine Learning Models
Chapter 7 Machine Learning Models for Smart Cities
173(30)
Dristi Datta
Nurul I. Sarkar
7.1 Introduction
173(4)
7.2 Machine Learning Frameworks
177(15)
7.2.1 Machine Learning Approaches
177(1)
7.2.2 Machine Learning Models
177(1)
7.2.2.1 Supervised Learning Models
178(1)
7.2.2.2 Unsupervised Learning Models
185(1)
7.2.2.3 Semi-Supervised Learning Models
189(1)
7.2.2.4 Reinforcement Learning Model
191(1)
7.3 Problem-Solving Using Machine Learning Techniques
192(2)
7.4 Smart City Design Infrastructure
194(2)
7.5 Smart City Design Challenges
196(2)
7.5.1 Technical Challenges
196(1)
7.5.2 Social Challenges
197(1)
7.5.3 Economic Challenges
197(1)
7.5.4 Miscellaneous Challenges
198(1)
7.6 Implications of ML Models in the Design of Smart Cities
198(1)
7.7 Concluding Remarks
199(4)
Chapter 8 Machine Learning Models in Smart Cities - Data-Driven Perspective
203(26)
Seyed Mandi Miraftabzadeh
Michela Longo
Federica Foiadelli
8.1 Introduction
204(1)
8.2 Artificial Intelligence and the Smart Cities
205(2)
8.3 Machine Learning
207(5)
8.3.1 Categories of Machine Learning Techniques
208(2)
8.3.2 Big Data and Machine Learning
210(2)
8.4 Data Terminology
212(1)
8.4.1 Data Definitions
212(1)
8.4.2 Data Type
212(1)
8.4.3 Dataset in Machine Learning
212(1)
8.5 Machine Learning Model
213(17)
8.5.1 Model Performance Analysis (Error)
214(2)
8.5.2 Validation Set
216(2)
8.5.3 Model Performance's Evaluation Metrics
218(1)
8.5.3.1 Classification Metrics
219(1)
8.5.3.2 Regression Metrics
221(1)
8.5.4 Machine Learning Algorithms
222(1)
8.5.4.1 Classification Algorithms
222(1)
8.5.4.2 Regression Algorithms
224(5)
Section V Case Studies and Future Directions
Chapter 9 The Use of Machine Learning Techniques for Monitoring of Photovoltaic Panel Functionality
229(36)
Haba Cristian-Gyozo
9.1 Introduction
230(2)
9.2 Solar Panel Monitoring
232(2)
9.3 Photovoltaic Operation Degradation
234(2)
9.4 Preventing Measures
236(1)
9.5 Real-Time Data Acquisition and Analytics
236(8)
9.5.1 Data Sources
236(1)
9.5.1.1 Local Data Acquisition Systems
237(1)
9.5.1.2 Meteorological Mini Stations
238(1)
9.5.1.3 Astronomical Data
238(1)
9.5.1.4 Cloud Services
241(1)
9.5.1.5 Alerting Systems
241(3)
9.6 Machine Learning Techniques in PV Panel Operation Monitoring
244(3)
9.7 Case study of System for PV Panel Monitoring
247(13)
9.7.1 Photovoltaic Systems in Romania
247(1)
9.7.2 Description of the Photovoltaic System
248(2)
9.7.3 Weather Station Prototype
250(1)
9.7.4 Data Sources
250(1)
9.7.4.1 Data from PV System
250(1)
9.7.4.2 Weather Ministations
251(1)
9.7.4.3 Cloud Services
251(1)
9.7.5 ML Methodology
252(1)
9.7.5.1 Data Collection
252(1)
9.7.5.2 Data Preprocessing
257(1)
9.7.5.3 Model Selection
257(1)
9.7.5.4 Feature Selection
257(1)
9.7.5.5 Training and Validation
257(3)
9.8 Conclusions
260(5)
Chapter 10 Intelligent Control System for Smart Environment Using Internet of Things
265(12)
Chintan Bhatt
Riya Patel
Siddharth Patel
Hussain Sadikot
Akrit Khanna
Esha Shah
10.1 Introduction
266(1)
10.2 Related Work
267(2)
10.3 Methodology
269(2)
10.3.1 Privileged Access
269(1)
10.3.2 Manual Control of Electrical Appliances
270(1)
10.3.3 Automatic Control of Electrical Appliances
271(1)
10.4 Experimental Set-up and Results
271(3)
10.5 Discussion and Conclusion
274(1)
10.6 Limitations
274(1)
10.7 Future Enhancements
274(3)
Chapter 11 Pathway and Future of IoE in Smart Cities
277
Sharda Tripathi
Swades De
11.1 Introduction
277(2)
11.2 IoE Application Case Studies
279(9)
11.2.1 Smart Monitoring of Civic Infrastructure and Amenities
280(1)
11.2.2 Smart Wireless Services
281(2)
11.2.3 Advanced Power Metering
283(2)
11.2.4 Smart Grid Monitoring
285(3)
11.3 Roles of Big Data and Context-Specific Learning in Future IoE
288(4)
11.3.1 Roles and Challenges of Big Data
288(1)
11.3.2 Node- and Network-Level Data-Driven Optimization
289(3)
11.4 Role and Challenges of Smart Grid in IoE Energy Sustainability
292(3)
11.4.1 Energy Sustainability
292(2)
11.4.2 Stability and Controllability of Power Grid
294(1)
11.5 Concluding Remarks
295
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.

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: 9781785618987), 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: 9789811387593), Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019) (ISBN 9789811533693). 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 editor 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-def ined-networking-smart-communities) and IEEE ICC 2020 (https://icc2020.ieee-icc.org/workshop/ws-06-secsdn-secure-and-dependable-sof tware-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.

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).