Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XXVI, 451 p. 178 illus., 165 illus. in color)

Edition: 1st ed. 2023

ISBN: 9783031460920

Series Statement: Intelligent Systems Reference Library 247

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-46091-3

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-46093-7

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-46094-4

Language: English

DOI: 10.1007/978-3-031-46092-0

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XXVI, 451 p. 178 illus., 165 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9783031460920

Series Statement: Intelligent Systems Reference Library, 247

Content: This book introduces big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as a huge amount of data collection are first discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this book. By dealing with a huge amount of data from electricity networks, meteorological information system, geographical information system, etc., many benefits can be brought to the existing power system and improve customer service as well as social welfare in the era of big data. However, to advance the applications of big data analytics in real smart grids, many issues such as techniques, awareness, and synergies have to be overcome. This book provides deployment of semantic technologies in data analysis along with the latest applications across the field such as smart grids.

Note: Data Analytics For Smart Grids And Applications - Present And Future Directions -- Design, Optimization and Performance Analysis of Microgrids using Multi-Agent Q-Learning -- Big Data Analytics for Smart Grid: A Review on State-of- Art Techniques and Future Directions -- Smart Grid Management for Smart City Infrastructure using Wearable Sensors -- Studies on Conventional and Advanced Machine Learning Algorithm towards Framing of Robust Data analytics for the Smart Grid Application -- Prediction and Classification For Smart Grid Applications -- A Review on Smart Metering Using Artificial Intelligence and Machine Learning Techniques: Challenges and Solutions.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031460913

Additional Edition: Printed edition: ISBN 9783031460937

Additional Edition: Printed edition: ISBN 9783031460944

Language: English

DOI: 10.1007/978-3-031-46092-0

URL: https://doi.org/10.1007/978-3-031-46092-0

UID:

Format: 1 online resource (466 pages)

Edition: 1st ed.

ISBN: 3-031-46092-8

Series Statement: Intelligent Systems Reference Library ; v.247

Note: Intro -- Preface -- About This Book -- Key Features -- Contents -- About the Editors -- 1 Data Analytics for Smart Grids and Applications-Present and Future Directions -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Smart Grid Infrastructure -- 1.4 Data Analytics in Smart Grids -- 1.4.1 Data Pre Processing Techniques in Smart Grids -- 1.4.2 Case Study of Data Analytics in Smart Grids -- 1.5 Artificial Intelligence in Smart Grids -- 1.5.1 Event Detection Using Data Analytics and Cloud Computing for Intelligent IoT System -- 1.6 Conclusion -- References -- 2 Design, Optimization and Performance Analysis of Microgrids Using Multi-agent Q-Learning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed Model -- 2.4 Experiments -- 2.5 Conclusion -- References -- 3 Big Data Analytics for Smart Grid: A Review on State-of-Art Techniques and Future Directions -- 3.1 Introduction -- 3.2 State-of-Art Techniques for Big Data Analytics in Smart Grids -- 3.3 Challenges in Big Data Analytics for Smart Grids -- 3.4 Big Data Analytics for Smart Grids -- 3.5 Applications of Big Data Analytics in Smart Grids -- 3.6 Challenges and Future Directions for Big Data Analytics in Smart Grids -- 3.7 Case Studies of Big Data Analytics in Smart Grids -- 3.7.1 Case Study 1: Duke Energy's Grid Modernization Program -- 3.7.2 Case Study 2: National Grid's Smart Grid Program -- 3.7.3 Case Study 3: ENEL's Smart Grid Program -- 3.8 Future Directions for Big Data Analytics in Smart Grids -- 3.9 Real-Time Big Data Analytics for Smart Grids -- 3.10 Conclusion -- References -- 4 Smart Grid Management for Smart City Infrastructure Using Wearable Sensors -- 4.1 Introduction -- 4.1.1 Smart Grid Versus Traditional Electricity Grids -- 4.1.2 Why Do We Need Smart Grids? -- 4.1.3 Smart Grid Features -- 4.1.4 Smart Grid Technologies -- 4.1.5 Smart Grid Approaches. , 4.1.6 Smart Meters and Home EMS -- 4.1.7 Smart Appliances -- 4.1.8 Home Power Generation -- 4.1.9 Machine Learning for Data Analytics in Smart Grids and Energy Management -- 4.1.10 Security for Industrial Control Systems in Smart Grids -- 4.1.11 Power Flow Modelling and Optimization in Smart Grids -- 4.1.12 Grid Stability and Security in Smart Grids -- 4.1.13 Integration of Renewable Energy Sources in Smart Grid Management -- 4.1.14 Demand Response Strategies for Efficient Smart Grid Management -- 4.1.15 Cybersecurity Measures for Smart Grid Management -- 4.1.16 Energy Storage Systems and Their Role in Smart Grid Management -- 4.1.17 Data Analytics and Artificial Intelligence in Smart Grid Management -- 4.1.18 Smart Grid Communication Protocols and Infrastructure -- 4.1.19 Advantages of Smart Grids -- 4.1.20 Disadvantages of Smart Grids -- 4.2 Conclusion -- References -- 5 Studies on Conventional and Advanced Machine Learning Algorithm Towards Framing of Robust Data Analytics for the Smart Grid Application -- 5.1 Introduction -- 5.2 Review of Different Smart Grid Based Approaches -- 5.3 Smart Grid Model -- 5.3.1 Smart Grids as Coordinators for Data Flow and Energy Flow -- 5.3.2 Big Data -- 5.4 Features of Big Data to Be Integrated into the Smart Grid -- 5.5 Contribution of the Smart Grid as Data Source -- 5.6 Smart Grid in Supply of Data Gathering -- 5.6.1 Data Transmission Methodology -- 5.6.2 Data Analysis Methodology -- 5.6.3 Data Extraction from Smart Grid -- 5.6.4 Grid for Production of Renewable Source of Energy -- 5.6.5 Big Data in Smart Grid -- 5.6.6 Machine Learning Approach to the Data Grid -- 5.6.7 Application of IOT to the Smart Grid Technology -- 5.7 IOT Based Solutions Towards Grid Problems -- 5.7.1 Stability of IOT Based Connection -- 5.7.2 Cost Effectiveness in Implementation -- 5.7.3 Security to the Information. , 5.8 Application of Data Grid in Mobile Sink Based Wireless Sensor Network -- 5.8.1 Assumptions of Network Characteristics -- 5.9 Virtual Grid Architecture -- 5.9.1 Different Structures of Virtual Grids -- 5.9.2 Virtual Grid Construction Cost -- 5.9.3 Reading of the Smart Meter Data and Its Analysis by the Smart Grid with Future Prediction -- 5.9.4 Prediction Analysis of Smart Meter Data -- 5.10 Future Research Direction -- 5.11 Conclusion -- References -- 6 Prediction and Classification for Smart Grid Applications -- 6.1 Introduction -- 6.2 Smart Grid -- 6.3 Predictive and Classification Models in Smart Grid Applications -- 6.4 Predictive Modeling -- 6.5 Classification Modeling -- 6.6 Smart Grid Management -- 6.7 Intelligent Data Collection Devices -- 6.8 Data Science Pertaining to Smart Grid Analytics -- 6.9 Machine Learning for Data Analytics -- 6.10 Data Security for Smart Grid Applications -- 6.11 Conclusion -- References -- 7 A Review on Smart Metering Using Artificial Intelligence and Machine Learning Techniques: Challenges and Solutions -- 7.1 Introduction -- 7.1.1 Trends of the Smart Metering Systems -- 7.1.2 Challenges of Smart Meters -- 7.1.3 Key Elements of Smart Meter -- 7.1.4 IoT in Smart Metering -- 7.1.5 Integration of IoT with AI and Machine Learning for Smart Meter -- 7.1.6 Artificial Intelligence Techniques -- 7.2 Conclusion -- References -- 8 Machine Learning Applications for the Smart Grid Infrastructure -- 8.1 Introduction -- 8.2 IoT in Distribution System -- 8.3 Techniques Using Machine Learning -- 8.4 Conclusion -- References -- 9 A Privacy Mitigating Framework for the Smart Grid Internet of Things Data -- 9.1 Introduction -- 9.1.1 Overview of the Smart Grid and Its Significance in Modern Energy Systems -- 9.1.2 Introduction to the IoT and Its Integration with the Smart Grid -- 9.1.3 Importance of Privacy in Smart Grid IoT Data. , 9.2 Privacy Challenges in Smart Grid IoT Data -- 9.3 Privacy Mitigation Techniques -- 9.4 Privacy Mitigation Framework for Smart Grid -- 9.4.1 Privacy Monitoring Engine Description -- 9.5 Results -- 9.6 Conclusion -- References -- 10 Protecting Future of Energy: Data Security and Privacy for Smart Grid Applications Using MATLAB -- 10.1 Introduction -- 10.1.1 Data Security and Privacy Threats -- 10.1.2 Data Security and Privacy Solutions -- 10.1.3 MATLAB Solution -- 10.1.4 Key Features and Capabilities -- 10.2 MATLAB Tools and Inbuilt Functions for Data Security in Applications of Smart Grid -- 10.3 MATLAB Functions for Data Security and Privacy in Smart Grid Applications Include -- 10.4 MATLAB Techniques for Data Security and Privacy in Smart Grid Applications -- 10.5 Matlab Algorithm for Privacy-Preserving Data Mining for Smart Grid Applications -- 10.6 Threats to Data Security and Privacy in Smart Grid Applications -- 10.6.1 Preventive Measures -- 10.7 Case Studies and Practical Implementations of Data Security and Privacy in Smart Grid Applications -- 10.7.1 Case Study 1: Securing Smart Meters Using Blockchain -- 10.7.2 Case Study 2: Machine Learning-Based Anomaly Detection in Power Grids -- 10.7.3 Case Study 3: Privacy-Preserving Data Aggregation in Smart Grids -- 10.7.4 Case Study 4: Secure Data Sharing in Smart Grids Using Homomorphic Encryption -- 10.7.5 Case Study 5: Anomaly Detection in Smart Grids Using Machine Learning (ML) with Matlab -- 10.8 Conclusion -- References -- 11 Revolutionizing Smart Grids with Big Data Analytics: A Case Study on Integrating Renewable Energy and Predicting Faults -- 11.1 Introduction -- 11.2 Current Trends in Smart Grid Based Big Data Analytics -- 11.2.1 There is a Notable Surge in Speculation in Smart Grid Projects and, Consequently, Smart Grid Analytics [9-11]. , 11.2.2 Smart Grid Analytics Effectively Handle Real-Time Data Despite the Increased Speed and Diverse Requirements -- 11.2.3 Digital Technologies and Cloud Computing Will Continue to Improve, Facilitating Enhanced Data Computation Capabilities -- 11.2.4 Smart Grid and Its Benefits for Renewable Energy -- 11.3 Challenges of Smart Grid Analytics -- 11.3.1 Benefits of Analytics in Smart Grid -- 11.3.2 Trends in the Utility Industry -- 11.4 Technologies for Smart Grid Analytics and Its Importance -- 11.4.1 Business Intelligence (BI) and Data Analysis -- 11.4.2 Other Framework Technologies-Databases Such as Apache Hadoop, MapReduce, and SQL -- 11.4.3 The Significance of Big Data in Smart Grid Analytics -- 11.5 Gaining Perceptions Through a Smart Grid and Big Data: A Case Study -- 11.5.1 Case Studies in Focus -- 11.5.2 Smart Grid Based Data Analytics Use-Cases in Europe -- 11.6 Future and Scope of Big Data Analytics in Smart Grids -- 11.6.1 Customer Acceptance and Engagement -- 11.6.2 Regulatory Policies -- 11.6.3 Innovative Structures -- 11.7 Conclusion -- References -- 12 Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques -- 12.1 Introduction -- 12.1.1 Statistics of Social Media Usage -- 12.1.2 Why Are Fake Profiles Created? -- 12.2 Literature Review -- 12.3 Proposed System for Detecting Fake Accounts on Twitter Using AI -- 12.3.1 Artificial Neural Network (ANN) -- 12.3.2 Support Vector Machine (SVM) -- 12.3.3 Random Forest (RF) -- 12.4 Findings and Discussions -- 12.5 Conclusion -- References -- 13 Data Analytics for Smart Grids Applications to Improve Performance, Optimize Energy Consumption, and Gain Insights -- 13.1 Introduction -- 13.2 Leveraging Smart Grids for Predictive Energy Analytics -- 13.3 Big Data Analytics for Grid Resiliency and Security. , 13.4 Machine Learning Techniques for Smart Grid Optimization.

Additional Edition: Print version: Kumar Sharma, Devendra Data Analytics for Smart Grids Applications--A Key to Smart City Development Cham : Springer International Publishing AG,c2024 ISBN 9783031460913

Language: English