ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids

作     者：Ceci, Michelangelo Corizzo, Roberto Japkowicz, Nathalie Mignone, Paolo Pio, Gianvito 

作者机构：Univ Bari Aldo Moro Dept Comp Sci I-70125 Bari Italy Jozef Stefan Inst Dept Knowledge Technol Ljubljana 1000 Slovenia Natl Interuniv Consortium Informat CINI Big Data Lab I-00185 Rome Italy Amer Univ Dept Comp Sci Washington DC 20016 USA 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：156053-156066页

核心收录：

基　　金：Ministry of University and Research (MUR) [ARS01_01259] U.S. Defense Advanced Research Projects Agency (DARPA) through the Project Lifelong Streaming Anomaly Detection [A19-0131-003] Ministry of University and Research (MUR) through the Project Big Data Analytics [AIM 1852414] 

主　　题：Smart grids Time series analysis Task analysis Feature extraction Data models Change detection algorithms smart grids one-class learning neural networks embedding 

摘      要：Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based CHAnge Detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections.