A non-intrusive load identification algorithm based on deep learning and a compound feature

Abstract

Aiming at the limitations of using a single feature for load identification, a non-intrusive load identification algorithm based on deep learning and compound features is proposed. The pixelated V-I trajectory characteristics and current harmonic characteristics are extracted by analyzing the load data under high-frequency sampling. Using the feature extraction capabilities of neural networks, the combination of pixelated V-I trajectory features and current harmonic features is realized. Finally, the composite feature is used as the new load feature to train the neural network for non-invasive load identification. The experimental results show that the two-layer neural network constructed by the algorithm can take advantage of the complementarity between the two features, thereby improving the load identification ability.