Incorporating Hurricane Forecast Uncertainty into a Decision-Support Application for Power Outage Modeling

Abstract

of decision-support systems, such as those employed by energy and utility companies, use the National Hurricane Center (NHC) forecasts of track and intensity to inform operational decision making as a hurricane approaches. Track and intensity forecast errors, especially just prior to landfall, can substantially impact the accuracy of these decision-support systems. This study quantifies how forecast errors can influence the results of a power outage model, highlighting the importance of considering uncertainty when using hurricane forecasts in decision-support applications. An ensemble of 1,000 forecast realizations is generated using the Monte Carlo wind speed probability model for Hurricanes Dennis, Ivan, and Katrina. The power outage model was run for each forecast realization to predict the spatial distribution of power outages. Based on observed power outage data from a Gulf Coast utility company, the authors found that in all three cases the ensemble average was a better predictor of power outages than predictions made using the official NHC forecast. The primary advantage of using an ensemble approach is that it provides a means to communicate uncertainty to decision makers. For example, the probability of a given number of outages and the potential range of power outages can be determined. Quantifying the uncertainty associated with the NHC official track and intensity forecasts can improve the real-time decisions made by governmental, public, and private stakeholders.