Dissolved gas analysis in oil is an effective method for early fault diagnosis of the transformer. Accurate prediction of dissolved gas volume fraction in oil can provide a theoretical basis for early fault monitoring and early warning of the transformer. In this paper

a prediction method of dissolved gases in transformer oil based on variational mode decomposition（VMD） and recurrent neural network with gated recurrent unit（GRU-RNN） was proposed. Firstly

the original time series of dissolved gases volume fraction in oil of transformer were decomposed into sub-series by VMD to reduce the influence of instability. Then

the GRU-RNN prediction method was used to predict the sub-series in one step and multi-step

respectively. Finally

the predicted sub-series were superposed and reconstructed to obtain the one-step and multi-step prediction of dissolved gas volume fraction in transformer oil. The results show that the mean absolute error（MAE） and root mean square error（RMSE） of the method are 0.057 6 and 0.068 4

respectively

and the MAE and RMSE of the multi-step prediction are 0.167 9 and 0.204 1

respectively. Compared with other prediction methods

the proposed method in this paper has a great improvement in single-step and multi-step prediction ability

which provides a reference for the power transformer monitoring and early warning.