000 03638cam a2200613Ii 4500
001 9781003047315
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007 cr cnu---unuuu
008 210616t20222022flua ob 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9781000411003
_qelectronic book
020 _a1000411001
_qelectronic book
020 _a9781000410983
_qelectronic book
020 _a1000410986
_qelectronic book
020 _a9781003047315
_qelectronic book
020 _a1003047319
_qelectronic book
020 _z0367497751
020 _z9780367497750
035 _a(OCoLC)1256628019
035 _a(OCoLC-P)1256628019
050 4 _aTD159.4
_b.I58 2022
072 7 _aCOM
_x037000
_2bisacsh
072 7 _aCOM
_x043000
_2bisacsh
072 7 _aTEC
_x007000
_2bisacsh
072 7 _aUT
_2bicssc
082 0 4 _a307.760285
_223
082 0 4 _a621.3102854678
_223
245 0 0 _aInternet of energy for smart cities :
_bmachine learning models and techniques /
_cedited by Anish Jindal, Neeraj Kumar and Gagangeet Singh Aujla.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2022.
264 4 _c©2022
300 _a1 online resource (xx, 302 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
520 _aMachine 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.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aSmart cities
_xTechnological innovations.
_912919
650 0 _aElectric power systems
_xAutomatic control.
_912920
650 0 _aElectric power distribution
_xData processing.
_912921
650 0 _aCities and towns
_xTechnological innovations.
_96680
650 0 _aInternet
_xIndustrial applications.
_912922
650 7 _aCOMPUTERS / Machine Theory
_2bisacsh
_912923
650 7 _aCOMPUTERS / Networking / General
_2bisacsh
_98506
650 7 _aTECHNOLOGY / Electricity
_2bisacsh
_912924
700 1 _aJindal, Anish,
_eeditor.
_912925
700 1 _aKumar, Neeraj
_c(Computer scientist),
_eeditor.
_912926
700 1 _aAujla, Gagangeet Singh,
_eeditor.
_912927
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003047315
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cEBK
999 _c70325
_d70325