Energy Spatial Pooling for Extremes Value Inference.

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Safety is an overriding priority in the nuclear industry; strict rules and regulations must be maintained to avoid nuclear disasters. Such disasters often result from extreme environmental processes, such as flooding or storms. This project is in collaboration with EDF, their nuclear research and development team focus on programmes supporting the safety, performance, and life extension of existing nuclear fleet. EDF must demonstrate that their power plants are robust to rare natural hazards. This involves studying unusually high or low levels of an environmental process, then using this extreme data to extrapolate beyond what is observed to provide an insight into the probability of future extreme events. The main statistical approach for understanding the risks associated with rare events is extreme value inference, where a statistical model is fit to the extreme values of a process. Estimates of the probabilities of future extreme events are subject to large amounts of uncertainty due to a lack of available data on such rare events. Reducing this uncertainty is desirable. Since data are usually available at multiple locations, it is sensible to try to incorporate this extra information into the inference at a single site. Additionally, we will explore the joint analysis of different environmental hazards, such as wind speed, sea level and rainfall. We plan to use these approaches for borrowing information to improve current methods for estimating extreme levels of a process.In partnership with EDF Energy.