### Related Works  
Accurate load prediction is very important for the energy generation, energy dispatching, and smooth operation of a power grid. It can also promote the maximum utilization of renewable resources and reduce the loss of primary energy in the power grid [15]. The user-side power grid, with residential quarters and commercial residences as the main components, is an effective carrier for local consumption and utilization of renewable energy. It undertakes the function of coordinating distributed power and user load [16]. According to existing statistics, the urban user-side power grid load reached more than 60% of the total non-industrial load of a city, becoming an important part of the urban power system [17]. In the power grid environment, due to the difference in consumption patterns among residents, the load prediction of group residents is more challenging than that of substations. [PERSON] et al. [18] classified electrical load based on forecast range into short-term load forecast, medium-term load forecast, and long-term load forecast. Short-term load forecasting models range from a few minutes to a few days. Daily load forecasts fall within the scope of short-term load forecasting. Short-term load forecasting is often used in a power grid to bridge the gap between energy generation and demand. Different from traditional residential load forecasting, load forecasting methods for a single building or a small number of buildings have challenges when dealing with the large number of users collected by smart meters. For example, cluster-based load forecasting methods have attracted more and more attention. In Ref. [19], a K-Means algorithm is applied to specific features of the load profile. They used the average consumption over a day, the average consumption for each day of the week, and the location of the peak over the year as feature clusters. The final sum of predictions is derived from the deep learning model. They improved the predictive accuracy of Irish datasets and smart meters in New York by 11%. In Ref. [20], a load prediction model based on improved fuzzy c-mean clustering is proposed. It filters out the weakly correlated features from adjacent load values and uses similar local daily data as input features. The above methods did not study th load forecasting of the group residents. The problems of large dimension of load data and obvious difference in load curve are not taken into account. Although clustering is a good solution, it can be optimized before making predictions. Because of the nonlinearity and nonstationarity in residential load data, it is difficult to describe the moving tendency of electric load and to improve the forecast accuracy. To establish a suitable and effective forecasting model, the original data features of the residential short-term electric load need to be fully considered and analyzed. In Refs. [21; 22], both papers propose a method for short-term electrical load prediction combining wavelet transforms and neural networks. The authors combine the wavelet transforms with neural networks for short-term electrical load forecasting. Despite the wavelet transforms becoming a standard for the analysis of nonlinear and nonstationary signals, there is still the problem of high prediction noise for computing and failure. In terms of predictive models, cutting-edge artificial intelligence methods represented by deep learning are developing rapidly. For example, one study [23] proposes an improved deep learning method that improves the accuracy and generalization ability of an infrared small object detection problem. In contrast to shallow learning, deep learning generally refers to stacking multiple layers of neural networks and relying on stochastic optimization to perform learning tasks. Among these methods, the convolutional neural network (CNN) is a type of feedforward neural network that includes convolutional calculations and has a deep structure [24; 25].