This paper studies the daily connectivity time series of a wind speed-monitoring network using multifractal detrended fluctuation analysis. It investigates the long-range fluctuation and multifractality in the residuals of the connectivity time series. Our findings reveal that the daily connectivity of the correlation-based network is persistent for any correlation threshold. Further, the multifractality degree is higher for larger absolute values of the correlation threshold.

The understanding of wind phenomena is of primary importance, especially regarding the renewable energy. One traditional way of wind modelling concerns the use of parametric probability distributions. This work presents a method in two steps to model this phenomena. The first of which studies and compares different extreme distributions by modelling data collected from the Swiss meteorological service. This comparison should serve as a basis for the second step, which applies spatial modelling of distribution parameters throughout the country. The modelling is performed in a high dimensional input space with the help of extreme learning machine. The knowledge of probability distributions allows us to give a comprehensive information about a wind speed distribution