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.
@article{arxiv.2302.14781,
title = {Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach},
author = {Somayeh Zamani and Hamed Talebi and Gunnar Stevens},
journal= {arXiv preprint arXiv:2302.14781},
year = {2023}
}