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.

功耗数据中的异常检测对于建筑物管理人员可能非常有用。它使他们能够检测出意外的功耗值，识别异常行为并预见异常事件。本文提出了一种新颖的无监督方法来检测功耗数据中的异常。我们将基于聚类的方法与基于预测的方法相结合，以学习典型的行为方案并预测下一小时的功耗。通过在24个不同的K上应用K -means算法来探索这些场景-表示每天24小时的分组。这是基于这样的假设：由于用户的生活习惯，相同的日常消费行为可能会反复出现。为了在异常发生前1小时检测到异常，已训练了长期短期记忆（LSTM）来预测下一个功耗值。将该预测值和一些较早的数据值连接在一起，然后将其与学习到的典型方案进行比较。我们通常使用自动编码器来检测异常日，并且使用这种新颖的方法来指定异常发生在什么时间。我们的方法不仅可以离线模式检测异常，还可以实时检测实时数据流。