To study the prediction of audible noise of transmission lines under the influence of multiple environmental factors at high altitudes

such three audible noise observation stations as 20 m

30 m and 35 m away from the side phase are set up under the 500 kV double-circuit line on the same tower at the altitude of 2 400 m. At the same time

the meteorological station is used to collect data on multiple environmental factors. In this paper

a kind of convolutional neural network (CNN)-bi-directional long short-term memory (BiLSTM) model based on the multi-head attention (MHA) mechanism is proposed for audible noise prediction. Firstly

the Pearson correlation analysis is used for calculation

comparison and elimination of correlation degree of various environmental factors data. Then

in order to fully mine the temporal features of the audible noise data

CNN is used for feature extraction of the multiple environmental factors data. Then

the extracted feature vectors are input to BiLSTM for training

and the multi-head attention mechanism is introduced at the BiLSTM side to make the model learn data features with higher weight

thus improving the prediction accuracy of the model. The results show that the combined model constructed by this method can improve the prediction accuracy of the audible noise data considering multi-factor features and has good generalization.