The Non-intrusive load monitoring using CNN: A time-series data approach

Abstract

To ensure safe and comprehensive power monitoring within equipment limits, Non-Intrusive Load Monitoring (NILM) is a leading solution due to its convenience and cost-effectiveness. Various NILM approaches exist, including statistical methods, non-electrical variable integration, and machine learning models. However, these methods often fail to fully capture load signal characteristics, particularly currentvoltage relationships. The Convolutional Neural Network (CNN) model is the most effective for monitoring and predicting load combinations using V-A (voltage-ampere) trajectory graphs. However, transient events can distort these trajectories, leading to incorrect predictions. This study enhances CNN performance by integrating time-series features to improve accuracy and handle switching events. Input data is structured as sequential images representing circuit states over time. A baseline model is designed, with multiple variations tested to analyze the correlation between parameters and accuracy. The study successfully applies a CNN model with time-series characteristics for load identification, even during switching events. Experimental results determine the most optimal model for practical applications.