2016/09/16 by Mohamed Laib, Laib, Mohamed, Mikhaïl Kanevski +1
Engineering · Environmental Science · #Atmospheric and Oceanic Physics (physics.ao-ph) #Data Analysis #Energy Load and Power Forecasting #FOS: Physical sciences #Statistics and Probability (physics.data-an) #Wind Energy Research and Development #Wind and Air Flow Studies
paper · pdf · doi:10.48550/arxiv.1609.05012
openalex publication_date 2016/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents an initial exploration of high frequency records of extreme wind speed in two steps. The first consists in finding the suitable extreme distribution for 120 measuring stations in Switzerland, by comparing three known distributions: Weibull, Gamma, and Generalized extreme value. This comparison serves as a basis for the second step which applies a spatial modelling by using Extreme Learning Machine. The aim is to model distribution parameters by employing a high dimensional input space of topographical information. The knowledge of probability distribution gives a comprehensive information and a global overview of wind phenomena. Through this study, a flexible and a simple modelling approach is presented, which can be generalized to almost extreme environmental data for risk assessment and to model renewable energy.