Uncertainty Visualization for Crisis Management in Smart Grid Environments

Visual analytics systems are in place within smart grid environments to alleviate crisis situations by allowing decision makers to perceive and understand the severity of a crisis situation. However, errors in measurements that are propagated due to various reasons (such as data transformations, errors in measurement devices etc.) can make the decision makers less confident in deriving information. Therefore, analysis and visualization of uncertainty within such data has become important. In this paper we utilize two uncertainty propagation techniques: sampling and Monte Carlo simulation, to propagate uncertainties inherent in power data within our smart grid environment, and compare their performance to best fit our use-case. We found that the Monte Carlo simulation method is most suitable for measuring uncertainty in our application domain. Further, we identified most effective visual metaphors to communicate uncertainty to the crisis managers.