Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate. 1. Introduction Nowadays, multifarious classification techniques are widely used in many signal-processing areas, such as face recognition, road traffic analyze, medical image, weather forecasting, and so forth [1, 2]. It has been considered whether this kind of techniques could serve the purposes of power load monitoring, which is a process for obtaining what appliances are used in the house as well as their individual energy consumption by analyzing changes in the voltage and current. In particular, information about the operating conditions of consumers (such as what appliances are operated and what state they are in) is useful for demand estimation and prediction. In addition, the operating conditions of electrical appliances in modern society clearly reflect consumers’ lifestyles and behavior patterns [3]. Research is being performed on the use of such information for circuit diagnoses, safety confirmation systems for elderly persons living alone [4], demand management, and optimization [5–10], and for other uses [11]. There are mainly two classes of approaches in power monitoring algorithm, including intrusive appliance load monitoring (IALM) and nonintrusive appliance load monitoring (NIALM) [12]. In order to measure consumption, IALM distributes direct sensors at each device or appliance. Although conceptually straightforward and potentially highly accurate, direct sensing is often expensive due to time consuming installation and the
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