Prediction and Analysis of Power User Behavior based on Data Mining

Traditional methods for predicting the behavior of power users mostly rely on simple historical data statistics, and the prediction accuracy is low, especially when the load fluctuates greatly. This paper proposes a method for predicting the behavior of power users based on data mining, which improves the prediction accuracy and intelligence level by deeply analyzing the power user data. First, the user's electricity consumption records are collected and sorted, and data cleaning and normalization are performed; then, cluster analysis is used to classify users and identify the electricity consumption characteristics of different groups; then, the support vector classifier algorithm is used to build a prediction model, and the parameters are optimized through cross-validation; finally, the classification tree algorithm is combined to post-process the prediction results to enhance the stability of the prediction. This method performs well in various load forecasting scenarios, especially in peak load forecasting, with an accuracy improvement of 10.4%. The method proposed in this paper can effectively enhance the accuracy of power load forecasting, and is particularly suitable for dynamic scheduling and load balancing of smart grids.