IGWO-LSSVM Transmission Line Icing Prediction Model Based on Grey Correlation Weight

In view of the problems in the existing ice prediction model for transmission tower lines, such as the random selection of input and output vectors, the low prediction accuracy, and the low prediction efficiency, an IGWO-LSSVM transmission line ice prediction model based on grey correlation weight is proposed. Firstly, the data reduction model is obtained by extracting the effective information in the historical micro-meteorological data based on the gray correlation analysis, determining the correlation weight between the micro-meteorological information and the icing formation, and selecting the data information with high correlation degree with the line icing as the feature vector. Secondly, the prediction model is obtained by calculating the penalty factor and kernel function width of Least Squares Support Vector Machines (LSSVM) by the Improved Gray Wolf Optimizer (IGWO). Finally, the simulation analysis of a transmission line icing in Jilin province shows that the ice prediction model proposed in this paper has high prediction accuracy and small error.