Internet of energy for smart cities : machine learning models and techniques / edited by Anish Jindal, Neeraj Kumar and Gagangeet Singh Aujla.
Contributor(s): Jindal, Anish [editor.]
| Kumar, Neeraj (Computer scientist) [editor.] | Aujla, Gagangeet Singh [editor.].
Material type: 
BookPublisher: Boca Raton, FL : CRC Press, 2022Copyright date: ©2022Edition: First edition.Description: 1 online resource (xx, 302 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9781000411003; 1000411001; 9781000410983; 1000410986; 9781003047315; 1003047319.Subject(s): Smart cities -- Technological innovations | Electric power systems -- Automatic control | Electric power distribution -- Data processing | Cities and towns -- Technological innovations | Internet -- Industrial applications | COMPUTERS / Machine Theory | COMPUTERS / Networking / General | TECHNOLOGY / ElectricityDDC classification: 307.760285 | 621.3102854678 Online resources: Taylor & Francis | OCLC metadata license agreement Summary: 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.
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.
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