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Activity Number: 423 - Recent Advancements in the Analysis of Extremes
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #328351 Presentation
Title: Extreme Wind Speed Forecasting Using INLA
Author(s): Daniela Castro* and Raphaƫl Huser
Companies: King Abdullah University of Science and Technology and KAUST
Keywords: Extreme value theory; Threshold-based inference; Latent Gaussian models; R-INLA; Wind speed forecasting; Wind power production

Renewable sources of energy such as wind power have become a sustainable alternative to fossil fuels-based energy. However, the uncertainty and fluctuation of the wind speed derived from its intermittent nature brings a great threat to the wind power production stability, and to the wind turbines themselves. A turbine cut-off point denotes how fast the turbine can go before turbine blades are brought to rest to prevent any damage produced by extreme wind speeds. In this work, we develop a flexible temporal model that comprises {in-site and off-site information} to 1) provide accurate short-term forecasts of wind speed and power, and 2) estimate the cut-off exceedance probability. Our model belongs to the wide class of latent Gaussian models and can handle non-extreme and extreme observations at the same time, accurately describing both the bulk and the tail of the wind speed distribution. Model parameters and predictive distributions are estimated taking advantage of the very powerful and efficient Integrate Nested Laplace Approximation methodology.

Authors who are presenting talks have a * after their name.

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