Arquitetura unificada para detecção, extração de características e classificação em sinais de NILM

Abstract

In the subsequent decades, the increasing energy will demand renewable resources and intelligent solutions for managing consumption. In this sense, Non-Intrusive Load Monitoring (NILM) techniques detail consumption information for users, allowing better electric power management and avoiding energy losses. This load monitoring technique usually requires four steps: event detection, signal disaggregation, feature extraction, and load identification. However, in high-frequency NILM methods, state-of-the-art approaches, mainly based on deep learning solutions, do not provide a complete NILM architecture, including all the required steps. To overcome this gap, this work presents an integrated method for detection, feature extraction, and classification of high-frequency NILM signals for the publicly available LIT-Dataset. In terms of detection, the results were above 90% for most cases, whilst the state-of-the-art methods were below 70% for eight loads. For classification, the proposed model performance on the evaluated metrics was comparable with other recent works, reaching above 97% for F-score and accuracy. The proposed architecture also includes a multi-label procedure to avoid the disaggregation stage, indicating the loads connected at a given time, increasing the recognition of multiple loads. This work also evaluates the proposed method's robustness to noise insertion. Finally, this work presents results in an embedded system, a subject also underexplored in the recent literature, demonstrating the proposal’s feasibility for real-time signal analysis and practical applications involving NILM.