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Applications of Big Data and Artificial Intelligence in Smart Energy Systems : Volume 1 Smart Energy System: Design and Its State-Of-the Art Technologies- [electronic resource]
Applications of Big Data and Artificial Intelligence in Smart Energy Systems : Volume 1 Smart Energy System: Design and Its State-Of-the Art Technologies- [electronic resource]
상세정보
- 자료유형
- 전자책(국외)
- 미국국회도서관 청구기호
- TK3105
- 자관 청구기호
- 기본표목-개인명
- 표제와 책임표시사항
- Applications of Big Data and Artificial Intelligence in Smart Energy Systems : Volume 1 Smart Energy System: Design and Its State-Of-the Art Technologies - [electronic resource] / Neelu Nagpal.
- 발행, 배포, 간사 사항
- 형태사항
- 1 online resource
- 총서사항
- River Publishers Series in Computing and Information Science and Technology Series
- 일반주기
- Description based upon print version of record.
- 일반주기
- 4.1.2.3: Gaussian mixture model (GMM)
- 내용주기
- 완전내용Cover -- 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
- 내용주기
- 완전내용1.6: 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
- 내용주기
- 완전내용2.3.7: 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
- 내용주기
- 완전내용2.5.1: 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
- 내용주기
- 완전내용3.2: 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)
- 요약 등 주기
- 요약This 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.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 부출표목-개인명
- 부출표목-개인명
- 부출표목-개인명
- 부출표목-개인명
- 기타형태저록
- Print version Nagpal Neelu Applications of Big Data and Artificial Intelligence in Smart Energy Systems
- 총서부출표목-통일표제
- River Publishers Series in Computing and Information Science and Technology Series.
- 전자적 위치 및 접속
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■24500▼aApplications of Big Data and Artificial Intelligence in Smart Energy Systems ▼bVolume 1 Smart Energy System: Design and Its State-Of-the Art Technologies▼h[electronic resource]▼cNeelu Nagpal.
■24630▼aSmart energy system
■260 ▼aMilton▼bRiver Publishers▼c2023.
■300 ▼a1 online resource
■4901 ▼aRiver Publishers Series in Computing and Information Science and Technology Series
■500 ▼aDescription based upon print version of record.
■500 ▼a4.1.2.3: Gaussian mixture model (GMM)
■5050 ▼aCover -- 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
■5058 ▼a1.6: 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
■5058 ▼a2.3.7: 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
■5058 ▼a2.5.1: 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
■5058 ▼a3.2: 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)
■520 ▼aThis 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.
■5450 ▼aDr. Neelu Nagpal (Senior Member, IEEE), an Associate Professor at GGSIP University, has been working for the last 16 years in the EEE Department of Maharaja Agrasen Institute of Technology, Delhi, India. Her Ph.D. in Electrical Engineering was accomplished at Delhi Technological University, Delhi. She was the recipient of a research award from Delhi Technological University during her Ph.D. course. She completed her Masters with distinction from Delhi University in Control and Instrumentation specialization. Besides having 22 years of experience in teaching, she has 5 years of industrial experience and a lot more educational contributions to her name. She has 20 research publications in top-tier journals and conferences and has a grant of one Australian patent. Her area of research is investigations into the dynamics, control, and estimation in the field of nonlinear stochastic systems (power systems and robotics), smart grid technologies, and artificial intelligence. She has been involved in many reputed conferences in various capacities such as advisor, reviewer, session-chair, track-chair, organizing committee member, and publication chair (IEEE conference-ICIERA-2021). She is an IEEE Smart Cities Ambassador, 2022. Professor Hassan Haes Alhelou (Senior Member, IEEE) is with Monash University, Australia. He is also a Professor at Tishreen University in Syria, and a consultant with Sultan Qaboos University in Oman. Previously, He was with UCD-Ireland and IUT-Iran. He was included in the 2018 and 2019 Publons list of the top 1% best reviewers in the world. He is the recipient of the Outstanding Reviewer Award from many journals, e.g., ECM, ISA Transactions, and Applied Energy; and the recipient of the best young researcher in the Arab Student Forum Creative among 61 researchers from 16 countries at Alexandria University, Egypt, 2011. He also received the Excellent Paper Award 2021 from IEEE CSEE Journal of Power and Energy Systems. He has published more than 200 research papers in high-quality peer-reviewed journals. His research papers have received 2850 citations with an H-index of 26 and an i-index of 56. He has authored/edited 15 books published by reputed publishers. He serves as an editor for a number of prestigious journals. He has participated in more than 15 international industrial projects over the globe. His major research interests are power system security and dynamics, power system cybersecurity, power system operation, and control, dynamic state estimation, and smart grids. Professor Pierluigi Siano (M'09-SM'14) received an M.Sc. degree in electronic engineering and a Ph.D. degree in information and electrical engineering from the University of Salerno, Salerno, Italy, in 2001 and 2006, respectively. He is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory with the Department of Management & Innovation Systems, University of Salerno, Italy. Since 2021 he has been a Distinguished Visiting Professor in the Department of Electrical & Electronic Engineering Science, University of Johannesburg. His research activities are centred on demand response, energy management, the integration of distributed energy resources in smart grids, electricity markets, and planning and management of power systems. In these research fields, he has co-authored more than 660 articles including more than 390 international journals that received in Scopus more than 14,200 citations with an H-index equal to 58. In 2019, 2020, and 2021 he was cited as a Highly Cited Researcher in Engineering by the Web of Science Group. He is the Editor for the Power & Energy Society Section of IEEE Access, IEEE Transactions on Power Systems, IEEE Transactions on Industrial Informatics, Transactions on Industrial Electronics, and IEEE Systems. Professor Sanjeevikumar Padmanaban (Member'12-Senior Member'15-IEEE) received his bachelor's, master and Ph.D. degrees in electrical engineering from the University of Madras, India, 2002, Pondicherry University, India, 2006, and the University of Bologna, Italy, 2012. Since 2018, he has been with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark, as an Assistant Professor. He has authored 300 plus scientific papers and has been involved in various capacities in many international conferences, including for IEEE and IET. He has received the best paper award from IET-SEISCON'13, IET-CEAT'16, and ETAEERE'16 sponsored Springer book publication series (five papers). He serves as an Editor/Associate Editor/Editorial Board of many refereed journals in particular the IEEE Systems Journal, IEEE Access, the IET Power Electronics, the subject editor of IET Renewable Power Generation, the subject editor of IET Generation, Transmission, and Distribution, Journal of Power Electronics (Korea) and FACETS (Canada). He is a fellow of the Institution of Engineers - FIE '18 (India) and a fellow of the Institution of Telecommunication and Electronics Engineers - FIETE'18 (India). Dr. D. Lakshmi is presently designated as a Senior Associate Professor in the School of Computing Science and Engineering (SCSE) and Assistant Director, Centre for Innovation in Teaching & Learning (CITL) at VIT Bhopal. She has 25 years of teaching experience. She has given innumerable guest lectures, acted as a session chair, and has been invited as a keynote speaker. She has conducted FDPs that cover approximately 50,000 plus faculty members including JNTU, TEQIP, SERB, SWAYAM, DST, AICTE, MHRD, ATAL, ISTE sponsored, and self-financed workshops across India. She has given 17 international conference presentations, has written 17 international journal papers including for SCOPUS, WOS, SCIE, SCI, 2 book chapters, 1 German patent, 3 Indian patents granted, 8 are yet to be granted, 2 Indian Copyrights granted, 4 Australian patents are granted, 8 more Indian patents are published and waiting for grant. A total of 18 patents are in various states. She has won two Best Paper awards at international conferences, and published a book on "Theory of Computation" in the year 2003 and "Leading Education in the Age of Disruption" in the year 2021.
■590 ▼aAdded to collection customer.56279.3
■650 0▼aSmart power grids.
■650 0▼aBig data▼xIndustrial applications.
■650 0▼aArtificial intelligence▼xIndustrial applications.
■650 6▼aRéseaux électriques intelligents.
■650 6▼aDonnées volumineuses▼xApplications industrielles.
■650 6▼aIntelligence artificielle▼xApplications industrielles.
■650 7▼aSCIENCE / Energy▼2bisacsh
■650 7▼aArtificial intelligence▼xIndustrial applications.▼2fast▼0(OCoLC)fst00817262
■650 7▼aSmart power grids.▼2fast▼0(OCoLC)fst01792824
■7001 ▼aAlhelou, Hassan Haes▼d1988-
■7001 ▼aSiano, Pierluigi.
■7001 ▼aSanjeevikumar, Padmanaban▼d1978-
■7001 ▼aLakshmi, D.
■77608▼iPrint version▼aNagpal, Neelu▼tApplications of Big Data and Artificial Intelligence in Smart Energy Systems▼dMilton : River Publishers,c2023▼z9788770228251
■830 0▼aRiver Publishers Series in Computing and Information Science and Technology Series.
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