Residential electricity consumption represents a large percentage of overall energy use. Therefore, accurately predicting residential electricity consumption and understanding the factors that influence it can provide effective strategies for reducing energy demand. In this study, a dendritic neural network-based model (DNM), combined with the AdaMax optimization algorithm, is used to predict residential electricity consumption. The case study uses the U.S. residential electricity consumption dataset.This paper constructs a feature selection framework for the dataset, reducing the high-dimensional data to 12 features. The DNM model is then used for fitting and compared with five commonly used prediction models. The $R^2$ of DNM is 0.7405, the highest among the six models, followed by the XGBoost model with an $R^2$ of 0.7286. Subsequently, the paper leverages the interpretability of DNM to further filter the data, obtaining a dataset with 6 features, and the $R^2$ on this dataset is further improved to 0.7423, resulting in an increase of 0.0018.