A load classification method based on data augmentation and few-shot machine learning

The volatility of renewable energy generation impacts the safe and stable operation of power systems. Moreover, load uncertainty complicates renewable energy consumption. Therefore, accurately extracting load patterns using artificial intelligence (AI) technology is crucial. Load classification is an effective way to master load behaviour. However, issues in the collected load data, such as data class imbalance, significantly affect the accuracy of traditional load classification. To address this problem, this study proposes a novel classification method based on data augmentation and few-shot learning, significantly enhancing the training efficiency of algorithm recognition. This addresses the challenge of real-data recognition in power systems. First, time-series load data are converted into images based on the Gramian angular field method to extract time-series data features using a convolutional neural network. Subsequently, the data are augmented based on variational autoencoder generative adversarial network to generate samples with distributions similar to those of the original data. Finally, the augmented few-shot data are classified using the embedding and relation modules of the relation network. A comparison of the experimental results reveals that the proposed method effectively improves power load classification accuracy, even with insufficient data. This paper presents a novel load classification method based on data augmentation and few-shot learning to effectively improve load classification accuracy. The few-shot power load classification method presented in this paper can accurately classify power loads using only 20 samples per class. It effectively improves energy efficiency and benefits renewable energy generation, transmission, and utilization. image