Variational AutoEncoder-Based Anomaly Detection Scheme for Load Forecasting

• Published in: Advances in Artificial Intelligence and Applied Cognitive Computing pp. 833 - 839
• Main Authors: Park, Sungwoo, Jung, Seungmin, Hwang, Eenjun, Rho, Seungmin
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• Series: Transactions on Computational Science and Computational Intelligence
• Subjects: Anomaly detection; Artificial neural network; Load forecasting; Variational autoencoder
• ISBN: 9783030702953; 3030702952
• ISSN: 2569-7072; 2569-7080
• DOI: 10.1007/978-3-030-70296-0_62

Smart grids can optimize their energy management by analyzing data collected from all processes of power utilization in smart cities. Typical smart grids consist of diverse systems such as energy management system and renewable energy system. In order to use such systems efficiently, accurate load forecasting should be carried out. However, if there are many anomalies in the data used to construct the predictive model, the accuracy of the prediction will inevitably decrease. Many statistical methods proposed for anomaly detection have had difficulty in reflecting seasonality. Hence, in this chapter, we propose VAE (Variational AutoEncoder)-based scheme for accurate anomaly detection. We construct diverse artificial neural network-based load forecasting models using different combinations of anomaly detection and data interpolation, and then compare their performance. Experimental results show that using VAE-based anomaly detection with a random forest-based data interpolation shows the best performance.