Elektrik sinyallerinden makine öğrenmesi ile cihaz kategorizasyonu

The growing need for energy management and sustainability necessitates innovative solutions in the analysis of electrical signals and device recognition. This study presents a novel approach for categorizing devices based on their unique energy consumption patterns using electrical signals. A TinyML-based system has been developed to enable resource-efficient and scalable device recognition on embedded platforms. The research addresses critical challenges in device recognition systems, such as signal noise, overlapping energy profiles, and real-time processing constraints. By leveraging advanced analytical techniques, the proposed system improves the accuracy and efficiency of device categorization. Additionally, it contributes to the broader adoption of TinyML in energy monitoring systems, providing significant benefits in terms of energy efficiency, fault detection, and sustainability. This study bridges the gap between academic innovation and practical application, supporting the development of smart home technologies, industrial automation, and intelligent energy management systems. The findings demonstrate the potential of integrating TinyML into energy monitoring frameworks, paving the way for more sustainable and user-friendly solutions.