Construction and application of power user portrait based on multifeature integration

In order to enhance the level of power grid services, understand the individual needs of different user groups, and optimize energy planning, a method for constructing and applying power user portraits based on multi-feature integration is proposed in this paper.By combining user social attributes, daily electricity consumption attributes, and quarterly electricity consumption attributes, features are extracted from multiple dimensions to construct a multi-dimensional user behavior label database.The k-means clustering algorithm is employed to construct power user portraits, and the CNN-LSTM model is applied to predict the total energy consumption of user groups under different feature label selection rules. This is done to assess the application potential of the proposed power user portrait construction method.The experimental results indicate that the proposed method provides accurate characterization of user group features, significantly improves energy consumption prediction accuracy, and effectively enhances the level of power grid services, assisting in energy dispatch decision-making.