Device Recognition from Electrical Signals with TinyML

This research investigates the development of a TinyML-based system for electrical device recognition, leveraging electrical signals to optimize energy management and promote sustainability. The study focuses on analyzing key metrics such as current, voltage, active power, and power factor to categorize devices accurately. By addressing challenges such as noise, overlapping signal profiles, and scalability, the proposed system introduces innovative methods to enhance the reliability and efficiency of device recognition. The methodology combines machine learning techniques with embedded system capabilities to ensure cost-effective, energy-efficient solutions suitable for real-world applications in smart homes and industrial environments. Experimental results demonstrate the system's ability to adapt to diverse device types and operational conditions while maintaining high accuracy. Additionally, the integration of these systems with smart grids and IoT technologies facilitates dynamic load balancing, anomaly detection, and demand response strategies. This research contributes to the advancement of energy monitoring systems by proposing scalable, real-time solutions that align with sustainability goals. Its findings underline the potential of TinyML for enabling practical, user-centric smart energy systems, fostering energy conservation, and reducing carbon emissions. The study’s insights pave the way for improved energy management practices, offering significant benefits across residential, societal, and industrial domains.