Big Data Platform for Smart Grids Power Consumption Anomaly Detection
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 2019 Federated Conference on Computer Science and Information Systems |
| MU Faculty or unit | |
| Citation | |
| web | https://ieeexplore.ieee.org/document/8859779 |
| Doi | http://dx.doi.org/10.15439/2019F210 |
| Keywords | Computer architecture; Big Data; Smart meters; Real-time systems; Power demand; Energy management; Anomaly detection |
| Description | Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark). |
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