Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the\r\nappliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an\r\nautomatic power load event detection and appliance classification based on machine learning. In power load event detection,\r\nthe paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately\r\ndetect the edge point when a device is switched on or switched off. The proposed load classification technique can identify\r\ndifferent power appliances with improved recognition accuracy and computational speed. The load classification method is\r\ncomposed of two processes including frequency feature analysis and support vector machine. The experimental results indicated\r\nthat the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more\r\ninformation than traditional NILM methods. The load classification method has achieved more than ninety percent recognition\r\nrate.
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