As smart meter technology advances, the advanced metering infrastructure (AMI) enables real-time monitoring of electricity usage at 15-minute intervals. Since prior studies mainly focused on industrial infrastructures, anomaly detection in residential electricity usage remains underexplored. In this paper, we propose a gated recurrent unit (GRU)-based deep learning model to detect resident changes in residential households using the electricity metering data through AMI. Monthly consumption patterns and energy consumption are predicted using actual electric energy metering data collected from 846 apartment households in Seoul, Korea. To detect the resident changes, combined losses are computed by integrating the consumption pattern and consumption energy prediction errors. The experimental results show an accuracy of 82.55%, specificity of 100%, and recall of 65.11% when allowing a +-1-month error margin. The proposed deep leaning model demonstrates the practical feasibility of detecting resident changes in residential households and is expected to contribute to the development of smart grid services.