Abstract
Abstract Non-intrusive Load Monitoring (NILM) technology is based only on the voltage at the total entrance of the power supply to the home and so on. The measurement and analysis of the current can obtain the electrical information of the electrical equipment in the scene. By detecting and classifying the load events, a NILM method based on the load events is proposed in the paper. The working state and power consumption of each electrical appliance in the scene can be inferred, and the non-intrusive load monitoring is realized. The method of edge detection was used to locate the load events generated by electrical equipment from the aggregated data. In order to improve the accuracy of monitoring, the voltage is increased on the basis of the power characteristics, and the support vector machine (SVM) was used as the classification model. Finally, the load events of various electrical appliances were collected and labeled in the field home data set. The accuracy of load event identification based on the classifier trained with different features is compared, and the effectiveness of the proposed method is verified.
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