Automatic power load event detection and appliance classification based on power harmonic features in nonintrusive appliance load monitoring

Abstract

Home electrical power monitoring plays an important role in reducing energy usage, and non-intrusive appliance load monitoring (NIALM) techniques are the most effective approach for estimating the electrical power consumption of individual appliances. Power load events detection is one of the most important steps in these techniques. This paper presents an automatic power load event detection method: edge symbol detector (ESD) for NIALM. The new transient detection approach can help the system locate all the load events (switch on and switch off) precisely. A modified power appliance classification technique based on power harmonic features and support vector machine (SVM), with higher recognition accuracy and faster computational speed, is also discussed. The experimental results of the new load events detection and classification technique are presented with promising results.