Applications of Big Data and Artificial Intelligence in Smart Energy Systems Volume 1 Smart Energy System: Design and Its State-Of-the Art Technologies.

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.

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Bibliographic Details
Other Authors Nagpal, Neelu, Alhelou, Hassan Haes, 1988-, Siano, Pierluigi, Sanjeevikumar, Padmanaban, 1978-, Lakshmi, D.
Format Electronic eBook
LanguageEnglish
Published Milton : River Publishers, 2023.
SeriesRiver Publishers Series in Computing and Information Science and Technology Series.
Subjects
Smart power grids.
Big data > Industrial applications.
Artificial intelligence > Industrial applications.
elektronické knihy
electronic books
Online AccessFull text
ISBN1000963837
9781003440710
1003440711
9781000963823
1000963829
9788770228244
8770228248
9781000963830
8770228256
9788770228251
Physical Description1 online resource (318 p.).

Cover

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
  • 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
  • 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
  • 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
  • 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)

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