  The increasing interest in renewable energy, particularly in wind, has given
rise to the necessity of accurate models for the generation of good synthetic
wind speed data. Markov chains are often used with this purpose but better
models are needed to reproduce the statistical properties of wind speed data.
In a previous paper we showed that semi-Markov processes are more appropriate
for this purpose but to reach an accurate reproduction of real data features
high order model should be used. In this work we introduce an indexed
semi-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 from
L.S.I. -Lastem station (Italy) and sampled every 10 minutes. We then generate
synthetic time series for wind speed by means of Monte Carlo simulations. The
time lagged autocorrelation is then used to compare statistical properties of
the proposed model with those of real data and also with a synthetic time
series generated though a simple semi-Markov process.
