Equipment identification from power load profile

The power load profile of the equipment varies with time, and it is essentially time series data. A new ensemble learning method for identifying electrical equipment through load power profile is proposed, which uses convolution neural network (CNN) as base learner to train the multi-granular load profile to improve the accuracy of classification. First, the raw data with different granularities are divided and some different new data sets are obtained. Then, these new data sets were used to train different base learners and get the weight of different base learners according to the accuracy of validation sets. In the testing process, testing data are divided based on different granularities in the same way as the training data are fed into base learners and the final results are obtained by weighting the output of each base learner. The proposed model are compared with a single CNN model on the electrical equipment load data. The experimental results show that the proposed method has higher accuracy in the identification of electrical equipment.