一种基于SARIMA-LSTM模型的电网主机负载预测方法

随着智能电网的不断发展，如何提高对信息设备运行状态的预测准确率以及设置适应数据变化的动态阈值区间是电网IT运维面临的巨大挑战。为了解决这些问题，提出了组合时间序列预测模型(SARIMA-LSTM),即在传统周期性ARIMA模型(SARIMA)的基础上，引入深度学习领域的LSTM模型，并摒弃了过去精度低、效果差的误差拟合方法，使用误差自回归方法来补偿预测结果。该模型可以学习到传统ARIMA模型无法捕捉到的误差波动规律，解决其无法预测非线性数据的问题。实验结果表明，在实际预测电网内存负载数据时，与ARIMA模型和SAIRIMA模型相比，SARIMA-LSTM模型可以实现更高的预测精度。

A load forecasting method for powergrid host based on SARIMA-LSTM model

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.