Research on Non-intrusive Load Identification Method Based on Support Vector Machine

Abstract

Basing on the hardware of smart electric power meter and aiming at the requirements of non-intrusive load identification application, the method of off-line learning and on-line classification based on support vector machine is studied. Two-sided cumulative sum based event detection method is employed to cut out the waveform windows between the start-up and shut-down of electric appliances, and then the characteristics of the electrical appliances are calculated. By using the feature data collected from electrical appliances samples, the parameters and support vectors of several binary classification decision functions are optimized through the MATLAB toolbox in an offline way. Then, a multiple classifier is constructed with several two class classifiers. Finally, the parameters of the multiple classifier are solidified into smart electric power meters to identify the type of electrical appliance load on-line. In order to verify the feasibility of the proposed method, the electrical appliance load classification test was carried out on the hardware platform of a single-phase electric power meter in the laboratory. The test results show that the accuracy rate of non-intrusive electrical appliance load identification is above 80%. The method proposed in this paper makes a large number of parameter optimization processes to be put on the computer, which is able to eliminate the online learning process and reduce the demand for calculation and storage resources of electric power meter, and has practical application value for the research of energy Internet on the user side.