A Contextual Anomaly Detection Framework for Energy Smart Meter Data Stream
In Neural Information Processing - Letters and Reviews, 2020
Abstract
Monitoring abnormal energy consumption is helpful for demand-side management. This paper proposes a framework for contextual anomaly detection (CAD) for residential energy consumption. This framework uses a sliding window approach and prediction-based detection method, along with the use of a concept drift method to identify the unusual energy consumption in different contextual environments. The anomalies are determined by a statistical method with a given threshold value. The paper evaluates the framework comprehensively using a real-world data set, compares with other methods and demonstrates the effectiveness and superiority.
Info
Conference Paper, 2020
In Neural Information Processing - Letters and Reviews, 2020
UN SDG Classification
DK Main Research Area
Science/Technology
