rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Applications of Big Data and Artificial Intelligence in Smart Energy Systems
    sub title: Volume 1 Smart Energy System: Design and Its State-Of-the Art Technologies
  title info:
    title: Smart energy system
  name:
    name part: Nagpal, Neelu.
  name:
    name part: Alhelou, Hassan Haes
    name part: 1988-
  name:
    name part: Siano, Pierluigi.
  name:
    name part: Sanjeevikumar, Padmanaban
    name part: 1978-
  name:
    name part: Lakshmi, D.
  type of resource: text
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: xx
    place:
      place term: Milton
    publisher: River Publishers
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic resource
    form: remote
    extent: 1 online resource (318 p.).
  abstract: This book covers the applications of various big data analytics, artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management.
  table of contents: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Figures -- List of Tables -- List of Contributors -- List of Abbreviations -- Chapter 1: Introduction to Smart Energy Systems in Recent Trends -- 1.1: Introduction -- 1.2: Global Emission -- 1.3: Evolution for Clean Energy: A Transition -- 1.4: Smart Energy System -- 1.5: Internet of Things (IoT) for Smart and Sustainable Future -- 1.5.1: IoT in smart city -- 1.5.2: IoT in agriculture -- 1.5.3: IoT in healthcare -- 1.5.4: IoT in smart grid and power management system
  table of contents: 1.6: Recent Developments in Smart Energy Systems -- 1.7: Conclusion and Future Measures to be Considered -- Chapter 2: An Overview of Artificial Intelligence, Big Data, and Internet of Things for Future Energy Systems -- 2.1: Introduction -- 2.2: Related Work -- 2.3: Sectors Involved in Energy Need Big Data, AI, and IoT -- 2.3.1: The energy sector requires big data -- 2.3.2: Big data techniques -- 2.3.3: Data communication techniques -- 2.3.4: Techniques for analyzing data -- 2.3.5: Data analytics techniques in smart grid -- 2.3.6: Mining data from a power system
  table of contents: 2.3.7: Modern grid systems and power consumption advanced analytics -- 2.4: Supporting Power Usage with IoT and Artificial Intelligence -- 2.4.1: The need for artificial intelligence in the renewable energy industry -- 2.4.2: Artificial intelligence (AI) research techniques classification -- 2.4.2.1: The use of computer-assisted learning systems -- 2.4.2.2: Fuzzy logic -- 2.4.2.3: Computer-aided translation -- 2.4.2.4: Robotics -- 2.4.2.5: Need of robotics in the energy sector -- 2.4.3: The energy sector requires IoT -- 2.5: Role of IoT in Energy Sectors
  table of contents: 2.5.1: IoT Impacts for the energy sector -- 2.5.2: Internet of Things applications in energy policy, economics, and production -- 2.5.2.1: Sectors of regulation and the market -- 2.5.2.2: Energy supply sector -- 2.5.2.3: Power transmission grids or energy grids -- 2.5.2.4: Energy demand sector -- 2.6: Future Energy Systems' Unsolved Problems -- 2.6.1: IoT energy sector challenges -- 2.6.2: Open challenges in AI energy sector -- 2.6.3: Open data analytics challenges in energy -- 2.7: Conclusion -- Chapter 3: LoRa: A New Technology for Smart Grid Applications -- 3.1: Introduction
  table of contents: 3.2: Related Work and Background -- 3.3: LoRa Challenges -- 3.4: LoRa Applications -- 3.5: Characteristics and Future Direction of LoRa Technology -- 3.5.1: Communication Range: -- 3.5.2: Placement of Multiple Gateways: -- 3.5.3: Link Coordination: -- 3.5.4: Security -- 3.5.5: Big data and AI -- 3.6: Case Study -- 3.7: Conclusion -- Chapter 4: Clustering Hybrid Application for Load Forecasting in Smart Grids -- 4.1: Introduction -- 4.1.1: Dataset pre-processing -- 4.1.2: Clustering techniques -- 4.1.2.1: K-means -- 4.1.2.2: Partitioning around medoids (PAM)
  note: Description based upon print version of record.
  note: 4.1.2.3: Gaussian mixture model (GMM)
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  subject:
    topic: Smart power grids
  subject:
    topic: Big data
    topic: Industrial applications
  subject:
    topic: Artificial intelligence
    topic: Industrial applications
  related item:
    title info:
      title: Applications of Big Data and Artificial Intelligence in Smart Energy Systems
    name:
      name part: Nagpal, Neelu
    origin info:
      publisher: Milton : River Publishers,c2023
  related item:
    title info:
      title: River Publishers Series in Computing and Information Science and Technology Series
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  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: EBLCP
    record creation date: 230729
    record change date: 20240717213016.0
    record identifier: kn-on1391443186
    language of cataloging:
      language term: eng