Analyzing the Benefits of Data Augmentation for Smart Grid Anomaly Detection and Forecasting
Xijuan Sun, Di Wu, Benoît Boulet · 2023
In view of the escalating electricity demand and the pervasive implementation of electrical appliances, the safe and efficient operation of smart grids has been recognized as of significant importance. In recent years, machine learning has been widely applied to smart grid core applications, i.e., anomaly detection and electric load forecasting. In order to achieve precise anomaly detection and accurate time series forecasting, a significant quantity of historical data is usually required for model training. In practice, however, acquiring such a sizable dataset is often accompanied by high costs and many challenges, making it impractical in real-world tasks. In this work, we employ data augmentation techniques to expand the training set size for smart grid anomaly detection and time series forecasting tasks. Specifically, we investigate the efficacy of noise injection and a generative adversarial networks based augmentation method on various machine learning-based models. Extensive experiment results on two real-world datasets demonstrate the effectiveness of data augmentation techniques on anomaly detection and time series forecasting tasks. Experiment results show the benefits of data augmentation and provide guidelines for researchers and engineers.