Nonintrusive Load Monitoring Using an LSTM With Feedback Structure

Many non-intrusive load monitoring (NILM) studies use high-frequency data to classify the device's ON/OFF state. However, these approaches cannot be applied in real-world situations due to increased network traffic and database capacity issues. For these reasons, when trying to perform NILM with low-frequency data, the power usage pattern that changes over time disappears and features cannot be properly obtained to classify devices. In this article, we propose a novel NILM model that can learn datasets with imbalanced data classes. The model extracts features using long-short term memory (LSTM) and improve the feature representation ability of LSTM through the feedback of predictions. The experiment is conducted using the REDD dataset and the Living-lab validation dataset. In the REDD dataset, the proposed method outperforms conventional methods 10%-20% on the Majority device and 50%-60% on the Minority device. Living-lab validation results show that the performance of the proposed method outperforms other previously proposed NILM systems in low-frequency data and can be applied to real-world NILM situations.