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

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.

Automated summary

This paper presents an integrated method for handling high-frequency NILM signals, including detection, feature extraction, and classification. The results show that the accuracy of this method is above 90% in most cases, surpassing state-of-the-art approaches, and it also includes a multi-label procedure to increase the recognition of multiple loads.