Smart grid data traffic behaves in a similar way to computer network data traffic and they are vulnerable to the same security risks. This paper presents a methodology for the real-time determination of adaptive estimates of router traffic-rate demand every five-minutes, as well as for the evolution of the estimated demand starting from zero during a five-minute interval using a Modified Mean Reverting Stochastic Process (M-MRSP). The determination of real-time adaptive estimates is based on an autoregressive model (AR(n)), which uses a window-size of past real-time router traffic-rate data with coefficients determined by a Kalman Filter (KF). The benefit of this technique is that potential monitoring tools could be provided with future knowledge of one 5-minute interval ahead. The methodology simulations show that the range of RMS error between the KF prediction and the internet service provider (ISP) measurements is of the order of 0.03, whereas the range of RMS error between the KF prediction and the M-MRSP values is of the order of 0.07. The synthetic time-series is a combination of M-MRSP and KF methodologies and maintains all the characteristics of a real traffic-rate time-series, such as non-stationarity, non-normality, long-range dependency (LRD), self-similarity and multimodality.

智能电网数据流量的行为与计算机网络数据流量的行为类似，并且容易遭受相同的安全风险。本文提出了一种方法，用于每五分钟实时确定路由器流量速率需求的自适应估计，以及使用修改后的平均回复随机数在五分钟间隔内从零开始的估计需求的演变流程（M-MRSP）。实时自适应估计的确定基于自回归模型（AR（n）），它使用过去的实时路由器流量数据的窗口大小，其系数由卡尔曼滤波器（KF）确定。这种技术的好处是可以为潜在的监视工具提供未来每5分钟间隔的未来知识。方法学仿真显示，KF预测和互联网服务提供商（ISP）测量之间的RMS误差范围约为0.03，而KF预测和M-MRSP值之间的RMS误差范围约为为0.07。综合时间序列是M-MRSP和KF方法学的结合，并保留了实际流量速率时间序列的所有特征，例如非平稳性，非正态性，远程相关性（LRD），自发性相似性和多模式性。