基于Autoformer的多因素气象条件下OPGW光缆寿命预测研究OA

To improve the accuracy of OPGW cable remaining life prediction,multi-factor meteorological data from Guangzhou between 2011 and 2023 are utilized to investigate cable degradation under temperature fluctuations and wind loads.A time series of cable length variation is constructed based on thermal expansion and contraction and mechanical load models,and an Autoformer-based remaining life prediction method is developed.Lagrange interpolation is applied to repair anomalous values,and Min-Max normalization is used for feature scaling.Comparison with LSTM,Bi-LSTM and Bi-LSTM+Attention models shows that Autoformer achieves optimal performance across RMSE,MAE,and MSE metrics,demonstrating a superior capability in capturing long-term variation trends of cable remaining length.The findings indicate that the proposed method enhances prediction accuracy and provides data-driven support for OPGW operation,maintenance,and replacement decision-making.