Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of “green” machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests.
Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation.
1. Chapter 1 Introduction to smart grid and the need for green solutions - p. 1 2. Chapter 2 Smart grid technologies through data preprocessing and feature engineering: role in demand response and green machine learning - p. 19 3. Chapter 3 An analysis of smart grid and management through data analytical models - p. 31 4. Chapter 4 Analysis and real-time implementation of power line disturbances test in smart grid - p. 49 5. Chapter 5 Energy efficiency and conservation using machine learning - p. 69 6. Chapter 6 Smart grid stability prediction using binary manta ray foraging-based machine learning - p. 79 7. Chapter 7 CO2 emissions: machine learning models for assessing the economic & environmental impact of fossil fuels and electric vehicles - p. 99 8. Chapter 8 Detection of electricity theft in Chinese power utility state grid corporation using hybrid deep learning model - p. 113 9. Chapter 9 Paradigm shift from machine learning to federated learning - p. 133 10. Chapter 10 Blockchain-backed design for an indestructible electric vehicle charging system - p. 147 11. Chapter 11 Thermal management of battery for electric vehicle - p. 159 12. Chapter 12 Optimal DG placement and FCL sizing using fuzzy-SSOA algorithm - p. 169 13. Chapter 13 Drive control strategies for PMSM drives in electric vehicles - p. 185 14. Chapter 14 Power management system for electric traction integrated with microgrid - p. 199 15. Chapter 15 A vehicle-to-grid and grid-to-vehicle off-board DC fast charger for an electric vehicle - p. 209 16. Chapter 16 The global energy landscape and the smart grid: a paradigm shift towards sustainability - p. 221 17. Chapter 17 Empowering a sustainable future: unleashing the potential of machine learning for energy efficiency and conservation - p. 235 18. Chapter 18 An analysis of IoT and machine learning–enabled smart grids for sustainable and future–pro energy management - p. 251 19. Chapter 19 Grid integration of renewable energy sources: challenges and solutions - p. 263 20. Index - p. 287
Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of “green” machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests.
Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation.
1. Chapter 1 Introduction to smart grid and the need for green solutions - p. 1 2. Chapter 2 Smart grid technologies through data preprocessing and feature engineering: role in demand response and green machine learning - p. 19 3. Chapter 3 An analysis of smart grid and management through data analytical models - p. 31 4. Chapter 4 Analysis and real-time implementation of power line disturbances test in smart grid - p. 49 5. Chapter 5 Energy efficiency and conservation using machine learning - p. 69 6. Chapter 6 Smart grid stability prediction using binary manta ray foraging-based machine learning - p. 79 7. Chapter 7 CO2 emissions: machine learning models for assessing the economic & environmental impact of fossil fuels and electric vehicles - p. 99 8. Chapter 8 Detection of electricity theft in Chinese power utility state grid corporation using hybrid deep learning model - p. 113 9. Chapter 9 Paradigm shift from machine learning to federated learning - p. 133 10. Chapter 10 Blockchain-backed design for an indestructible electric vehicle charging system - p. 147 11. Chapter 11 Thermal management of battery for electric vehicle - p. 159 12. Chapter 12 Optimal DG placement and FCL sizing using fuzzy-SSOA algorithm - p. 169 13. Chapter 13 Drive control strategies for PMSM drives in electric vehicles - p. 185 14. Chapter 14 Power management system for electric traction integrated with microgrid - p. 199 15. Chapter 15 A vehicle-to-grid and grid-to-vehicle off-board DC fast charger for an electric vehicle - p. 209 16. Chapter 16 The global energy landscape and the smart grid: a paradigm shift towards sustainability - p. 221 17. Chapter 17 Empowering a sustainable future: unleashing the potential of machine learning for energy efficiency and conservation - p. 235 18. Chapter 18 An analysis of IoT and machine learning–enabled smart grids for sustainable and future–pro energy management - p. 251 19. Chapter 19 Grid integration of renewable energy sources: challenges and solutions - p. 263 20. Index - p. 287
