Book chapter · Journal article
Online Anomaly Energy Consumption Detection Using Lambda Architecture
Department of Management Engineering, Technical University of Denmark1
Systems Analysis, Department of Management Engineering, Technical University of Denmark2
DTU Climate Centre, Systems Analysis, Department of Management Engineering, Technical University of Denmark3
University College of Northern Denmark4
Department of Civil Engineering, Technical University of Denmark5
Section for Building Energy, Department of Civil Engineering, Technical University of Denmark6
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark7
With the widely use of 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 refreshing the detection models from scalable data sets, but also real-time anomaly 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 lambda detection system.
| Language: | English |
|---|---|
| Publisher: | Springer International Publishing |
| Year: | 2016 |
| Pages: | 193-209 |
| Proceedings: | 18th International Conference on Big Data Analytics and Knowledge Discovery |
| ISSN: | 16113349 and 03029743 |
| Types: | Book chapter and Journal article |
| DOI: | 10.1007/978-3-319-43946-4_13 |
| ORCIDs: | Liu, Xiufeng , Nielsen, Per Sieverts and Heller, Alfred |
