Anomalies are patterns in data that do not follow the expected behaviour and they are rarely encountered. Anomaly detection has been widely used within diverse research areas such as credit card fraud detection, image processing, and many other application domains. In this paper, we focus on detecting anomalies in power consumption data. The identification of unusual behaviours is important in order to foresee uncommon events and to improve energy efficiency. To this end, we propose a model to precisely identify anomalous days and another one to localize the detected anomalies. Normal days are identified using a simple Auto-Encoder reconstruction technique, whereas the localization of the anomaly throughout the day is performed using a combination of LSTM and K-means algorithms. This hybrid model that combines prediction and clustering techniques, permits to detect unusual behaviour based on the assumption that identical daily consumption can appear repeatedly due to users’ living habits. The model is evaluated using real-world power consumption data collected from Pecanstreet in the United States.