A Classification Method for Electricity Users Based on the LightGBM Algorithm

Abstract

Providing customized services for different users is of great significance in the development process of smart grids. In order to classify electricity users more accurately, based on GBDT algorithm, the lightgbm model was used to classify users. The preprocessed dataset was used to train the lightgbm model, and bayesian methods were used to optimize the hyperparameters of lightbgm. The experimental result shows that the final lightgbm model has a significantly higher prediction accuracy than other models, and can effectively achieve user classification tasks.