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:此外,它还协助电网运营商安排其资源,以满足终端用户的实际需要,同时帮助终端用户降低能源成本。在本文中,我们分析了REFIT数据集两栋楼使用的洗碗机的电耗模式。然后,用1D-CNN和TCN作为主干器的两个自动编码器(AEs)进行了培训,以区分正常模式和异常模式。我们的结果表明,TCN在发现能源消耗异常方面超过了CNN1D。最后,Fridge_Freezer和REFIT第3号房冷冻器的数据也用于评估我们的方法。</s>