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.

翻译：由不同异常引起的能源泄漏问题，其修复可带来显著的能源节约并延长设备寿命。此外，这有助于电网运营商调度资源以满足终端用户的实际需求，同时帮助终端用户降低能源成本。本文分析了REFIT数据集中两户家庭洗碗机用电模式，随后训练了以1D-CNN和TCN为骨干网络的两个自编码器（AEs），用于区分正常模式与异常模式。结果表明，TCN在检测用电异常方面优于CNN1D。最后，采用REFIT中第三户家庭的Fridge_Freezer及Freezer数据对所提方法进行评估。