DeepDFML-NILM: A New CNN-Based Architecture for Detection, Feature Extraction and Multi-Label Classification in NILM Signals

• Published in: IEEE sensors journal Vol. 22; no. 1; pp. 501 - 509
• Main Authors: Nolasco, Lucas da Silva, Lazzaretti, Andre Eugenio, Mulinari, Bruna Machado
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Convolutional neural networks; Deep learning; embedded system; Embedded systems; Feature extraction; Load modeling; Machine learning; multi-label; Neural networks; NILM; Power consumption; Power management; Renewable resources; Signal analysis; Signal classification; Transfer learning; Voltage measurement
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3127322

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. 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 final accuracies were comparable with other recent works (around 97%). We also include a multi-label procedure to avoid the disaggregation stage, indicating the loads connected at a given time, increasing the recognition of multiple loads. Finally, we present 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.