Smart Device Recognition: Ubiquitous Electric Internet of Things

Advanced Device Recognition for the Smart Grid Era

The rise of the Ubiquitous Electric Internet of Things (UEIOT) has transformed how electrical grids sense, monitor, and interact with devices. This book offers a focused look at how smart device recognition can be achieved without intrusive sensors by analyzing aggregated electrical signals and applying modern data science techniques.

From Physical Methods to Intelligent Learning

Traditional device recognition often relied on physical signatures or template matching. While useful, these methods can struggle with variability and complexity. The authors introduce a range of intelligent methods that learn from data, including decision trees, support vector machines, extreme learning machines, neural networks, clustering algorithms, and multi-label classification approaches. Each chapter provides theoretical foundations, model frameworks, and detailed experimental results.

Practical Techniques for Researchers and Engineers

Readers will find practical guidance on building non-intrusive load monitoring systems. The book covers data acquisition, event detection, feature extraction, and load identification, and it illustrates how these components work together in a UEIOT framework. The emphasis is on reproducible experiments and comparative analysis, making it a valuable resource for those developing intelligent energy management solutions.

Who This Book Is For

This ebook is particularly relevant for doctoral students, researchers, and practicing engineers working in smart grid technology, energy conservation, electrical engineering, or applied machine learning. It is also useful for professionals exploring the intersection of IoT, data science, and power systems.

Available at Digital Delights as a digital edition.