Classification of power loads based on an improved denoising deconvolutional auto-encoder

Abstract

Machine learning has a wide range of applications in the recognition of power loads (PLs). In the light of the problems, such as poor generalization and the ease of falling into the local optima existing in the current PL classification algorithms, an improved algorithm based on the denoising deconvolutional auto-encoder was proposed to classify the field PL data. With the mirror symmetric structure of the network, the convolutional module can extract the distinctive features, while the deconvolutional module can reduce data redundancy and maintain high activation pixels. The data preprocessing accomplishes data dimensionality reduction. In order to accelerate convergence and improve classification accuracy, the unsupervised pre-training and ℓ2, regularization were used. The experimental results in the field data of a provincial power grid demonstrate that the proposed algorithm has a better generalization performance and a higher recognition rate than other algorithms, thus providing an efficient and objective way for PLs recognition.