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
Online AccessFull text
ISBN1000963837
9781003440710
1003440711
9781000963823
1000963829
9788770228244
8770228248
9781000963830
8770228256
9788770228251
Physical Description1 online resource (318 p.).

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245 0 0 |a Applications of Big Data and Artificial Intelligence in Smart Energy Systems  |h [electronic resource] :  |b Volume 1 Smart Energy System: Design and Its State-Of-the Art Technologies. 
246 3 0 |a Smart energy system 
260 |a Milton :  |b River Publishers,  |c 2023. 
300 |a 1 online resource (318 p.). 
490 1 |a River Publishers Series in Computing and Information Science and Technology Series 
500 |a Description based upon print version of record. 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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) 
500 |a 4.1.2.3: Gaussian mixture model (GMM) 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Smart power grids. 
650 0 |a Big data  |x Industrial applications. 
650 0 |a Artificial intelligence  |x Industrial applications. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Nagpal, Neelu. 
700 1 |a Alhelou, Hassan Haes,  |d 1988- 
700 1 |a Siano, Pierluigi. 
700 1 |a Sanjeevikumar, Padmanaban,  |d 1978- 
700 1 |a Lakshmi, D. 
776 0 8 |i Print version:  |a Nagpal, Neelu  |t Applications of Big Data and Artificial Intelligence in Smart Energy Systems  |d Milton : River Publishers,c2023  |z 9788770228251 
830 0 |a River Publishers Series in Computing and Information Science and Technology Series. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpABDAISE4/applications-of-big?kpromoter=marc  |y Full text