English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The increasing interest in renewable energy, particularly in wind, has givenrise to the necessity of accurate models for the generation of good syntheticwind speed data. Markov chains are often used with this purpose but bettermodels are needed to reproduce the statistical properties of wind speed data.In a previous paper we showed that semi-Markov processes are more appropriatefor this purpose but to reach an accurate reproduction of real data featureshigh order model should be used. In this work we introduce an indexedsemi-Markov process that is able to fit real data. We downloaded a database,freely available from the web, in which are included wind speed data taken fromL.S.I. -Lastem station (Italy) and sampled every 10 minutes. We then generatesynthetic time series for wind speed by means of Monte Carlo simulations. Thetime lagged autocorrelation is then used to compare statistical properties ofthe proposed model with those of real data and also with a synthetic timeseries generated though a simple semi-Markov process.

Wind speed modeled as an indexed semi-Markov process