Advanced Anomaly Detection Technologies And Applications In Energy Systems

Advanced Anomaly Detection Technologies and Applications in Energy Systems

Anomaly detection is an important topic which has been well‐studied in diverse research areas and application domains. It generally involves detection of abnormal data, unhealthy status, fault diagnosis, and can be helpful to guarantee industrial systems’ stability, security, and economy. As development of intelligent industries and sensor systems grows, large amounts of data become easily available, and challenges arise in industrial systems’ anomaly detection. One typical case is the study within energy‐related systems, like thermal energy, renewable energy study (e.g., wind energy, photovoltaic), electric vehicles, and so on. These systems can involve various data formats and more complex data structures making anomaly data detection a challenge. Currently, under the development of deep learning and big data analytics, many promising results have been achieved in energy systems’ anomaly data detection. However, many challenging problems remain unsolved due to the complex nature of energy industries. New techniques and advanced engineering applications on anomaly detection in energy systems still appeal to a wide range of scholars and industries.

Energy and Water Development Appropriations for 1996

Green Machine Learning and Big Data for Smart Grids

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. - Packages core concepts of green machine learning and smart grids in a clear, understandable way - Includes real-world, practical applications and case studies for replication and innovative solution development - Introduces readers with a range of expertise to best practices and the latest technological advances