During the operation of the power grid, key devices such as converter valves continue to generate heat, affecting the stability and safety of the system. Then it is crucial to ensure the stable operation of those devices. As a major component in the cooling system, the valve cooling system releases the heat energy from the equipment with water of high thermal conductivity as the medium. The stable operation of the converter valve can be ensured by monitoring the temperature and pressure of the cooling water. Also, with inlet valve temperature in the valve cooling system as the main predictive index, the historical data of the system is fully mined for predicting the operating state of the power grid. An ARIMA-SVM hybrid model integrating the traditional time series model and machine learning is compared with the traditional ARIMA model, the SVM model and the GRU neural network model with regard to the time series analysis of the real valve cooling data from China Southern Power Grid. The comparative experimental results demonstrate that the above four models can all clearly indicate the trend of the inlet valve temperature. However, the ARIMA-SVM hybrid model behaves better in the evaluation indicators including the root mean square error, the mean square error and the mean absolute error than the other three, with a more accurate prediction of inlet valve temperature.