User Power Data Mining Based on Deep Learning and Improved Grasshopper Optimization Algorithm
The data mining of power consumption characteristics of massive users is studied,and a user power data mining method based on deep learning and improved grasshopper optimization algorithm is proposed.The Pearson correlation coeffi-cient method is used to select the feature set of power consumption behavior to minimize the dimension of data processing.The linear weighted KFCM algorithm is designed,and the grasshopper optimization algorithm is used to initialize the cluster center and the number of clusters,so as to improve the clustering effect.The improved KFCM is used for data mining of users'power consumption behavior to provide data support for power grid enterprise network decision-making.Simulation results show that the proposed method has a good performance in clustering effect.
万方数据
维普
