A non-invasive load identification method based on data association learning

In order to optimize the performance of load identification, a non-invasive load identification method based on data association learning is proposed. Firstly, collect harmonic components and extract harmonic component information from the electricity data. Then the methods of missing value filling, Outlier processing and normalization are used to preprocess the data to improve the accuracy of load identification. Finally, a non-invasive load identification method based on data association learning was adopted. Through experiments, it was found that the non-invasive load identification method based on data association learning has high accuracy and reliability, and does not require the installation of additional sensors, which will not affect the normal electricity consumption of users, and has high practical value.