Paper Info
Title
Regression-based Online Anomaly Detection for Smart Grid Data.
Abstract
With the widely used smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative detection model refreshment from scalable data sets, but also real-time detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the proposed lambda detection system.
Year
Venue
Field
2016
Energy
Data mining,Anomaly detection,Data set,Data stream mining,Spark (mathematics),Smart grid,Computer science,Supervised learning,Big data,Scalability
DocType
Volume
Citations 
Journal
abs/1606.05781
2
PageRank 
References 
Authors
0.36
22
2
Name
Order
Citations
PageRank
Xiufeng Liu110814.69
Per Sieverts Nielsen2263.83