Our project focuses on enhancing the safety and efficiency of electrical monitoring in student dormitories through a non-intrusive residential load disaggregation system. This system leverages a transformer encoder for feature extraction and utilizes a memory bank to store these features for anomaly detection. By automating the detection process, the project promises to improve residential power management significantly, ensuring high accuracy and reliability without the need for manual intervention.
