Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach
This work addresses energy leakage issues for smart home users and grid operators, but it is incremental as it applies existing deep learning methods to a specific dataset.
The paper tackled anomaly detection in smart home energy consumption using autoencoders with 1D-CNN and TCN backbones, finding that TCN outperformed 1D-CNN in detecting anomalies on the REFIT dataset.
Fixing energy leakage caused by different anomalies can result in significant energy savings and extended appliance life. Further, it assists grid operators in scheduling their resources to meet the actual needs of end users, while helping end users reduce their energy costs. In this paper, we analyze the patterns pertaining to the power consumption of dishwashers used in two houses of the REFIT dataset. Then two autoencoder (AEs) with 1D-CNN and TCN as backbones are trained to differentiate the normal patterns from the abnormal ones. Our results indicate that TCN outperforms CNN1D in detecting anomalies in energy consumption. Finally, the data from the Fridge_Freezer and the Freezer of house No. 3 in REFIT is also used to evaluate our approach.