Abstract: With the continuous development of smart grids, how to improve the prediction effect of the future operation status of information equipment and set the dynamic threshold interval to adapt to data changes are huge challenges for IT operation and maintenance of power grid. In order to solve these problems, this paper proposes a combined time series forecasting model (SARIMA-LSTM). On the basis of the traditional periodic ARIMA (SARIMA) model, the LSTM model in the field of deep learning is introduced, which discards the low accuracy and poor effect of the traditional error fitting method using error autoregressive method to compensate the prediction result. By using this model to do prediction, we can learn the error fluctuation law which cannot be captured by the traditional ARIMA model, and make up for its inability to predict nonlinear data. Finally, the experimental results show that, compared with the ARIMA model and the FAIRIMA model, the SARIMA-LSTM model can achieve higher prediction accuracy, when actually predicting the grid memory load data.

Key words: time series, load forecasting, seasonal auto regressive integrated moving average(SARIMA), error compensation, long short-term memory(LSTM)