A deep learning approach for anomaly detection and prediction in power consumption data

Anomaly detection in power consumption data can be very useful to building managers. It allows them to detect unexpected power consumption values, identify unusual behaviors, and foresee uncommon events. This paper proposes a novel unsupervised approach to detect anomalies in power consumption data. We combine the clustering-based methods with the prediction-based ones to learn typical behavior scenarios and to predict the power consumption of the next hour. These scenarios are explored by applying the K-means algorithm on 24 different K-means groups representing the 24 h of the day. This is based on the assumption that identical daily consumption behavior can appear repeatedly due to users’ living habits. In order to detect the anomaly 1 h before its occurrence, a Long Short-Term Memory (LSTM) has been trained to predict the next power consumption value. This predicted value with some earlier data values are concatenated into a vector then compared with the learned typical scenarios. We used Auto-Encoders to detect anomalous days in general and this novel method to specify at what time the anomaly has occurred. Our approach not only detectss anomalies in off-line mode but also allows real-time detection on live data streams.