  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.
