An electrification revolution in the Chinese building energy field has been promoted by China's carbon peak and carbon neutrality goals. An accurate electricity load prediction was essential to resolve the conflict between substations which was caused by the current increase in energy demand, on both the generation and consumption sides. This review provided an in-depth study of prediction models for residential building electricity load and inspected various output types, prediction methods and driving factors. The prediction types were divided into three categories: (i) time scale, (ii) geographical scale and (iii) regional scale. Predictive model building methods were classified as classical, algorithms based on Machine Learning (ML) or Deep Learning (DL) and hybrid methods. Driving factors included single and multiple features. By summarizing the driving factors, the influence of improving the prediction accuracy according to the characteristics of output types on selecting the driving factors correctly was discussed. The review provided a key perspective for future studies in electricity load prediction by analyzing the regional variations in electricity load characteristics. It suggested that the regional electricity load prediction model for residential buildings based on diverse driving factors in each region was established to offer valuable solutions for future residential planning and energy distribution.