Online real-time robust framework for non-intrusive load monitoring in constrained edge devices

Abstract

Real-time information on detailed power consumption can motivate users to make informed decisions to reduce their energy bills. In that sense, Non-Intrusive Load Monitoring (NILM) emerges as a cost-effective technique to achieve the previously mentioned benefits. This paper presents an online real-time robust NILM framework that only requires the aggregated active power, operates by updating the appliance’s state probabilities sequentially, and uses this information to predict the power consumption of each monitored appliance. The framework primarily focuses on the seamless integration and practical deployment of a real-time NILM algorithm, operating at frequencies around 1 Hz, on constrained edge devices. Starting with detecting edges and the base load in real-time, the appliance’s state probabilities are updated considering the possible presence of unknown loads. The power consumption of each appliance is then estimated by employing a modified Population-Based Incremental Learning algorithm (PBIL). Experiments on two publicly available datasets against state-of-the-art methods demonstrated its accuracy and robustness in the presence of unknown appliances. The real-time capabilities of the framework were verified through integration in a Home Automation framework running in a constrained edge device.