Spatial Patterns of Wind Speed Distributions in Switzerland

TL;DR

This study identifies suitable extreme wind speed distributions across Swiss stations and models their spatial patterns using machine learning, providing insights for risk assessment and renewable energy planning.

Contribution

It introduces a combined approach of distribution fitting and spatial modeling with Extreme Learning Machine for wind speed analysis.

Findings

Weibull distribution best fits the wind data at most stations.

The spatial model accurately predicts distribution parameters across Switzerland.

The approach is adaptable for other environmental extreme data.

Abstract

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.