19 research outputs found
Wind speed forecasting at different time scales: a non parametric approach
The prediction of wind speed is one of the most important aspects when
dealing with renewable energy. In this paper we show a new nonparametric model,
based on semi-Markov chains, to predict wind speed. Particularly we use an
indexed semi-Markov model, that reproduces accurately the statistical behavior
of wind speed, to forecast wind speed one step ahead for different time scales
and for very long time horizon maintaining the goodness of prediction. In order
to check the main features of the model we show, as indicator of goodness, the
root mean square error between real data and predicted ones and we compare our
forecasting results with those of a persistence model
Reliability measures of second order semi-Markov chain applied to wind energy production
In this paper we consider the problem of wind energy production by using a
second order semi-Markov chain in state and duration as a model of wind speed.
The model used in this paper is based on our previous work where we have showed
the ability of second order semi-Markov process in reproducing statistical
features of wind speed. Here we briefly present the mathematical model and
describe the data and technical characteristics of a commercial wind turbine
(Aircon HAWT-10kW). We show how, by using our model, it is possible to compute
some of the main dependability measures such as reliability, availability and
maintainability functions. We compare, by means of Monte Carlo simulations, the
results of the model with real energy production obtained from data available
in the Lastem station (Italy) and sampled every 10 minutes. The computation of
the dependability measures is a crucial point in the planning and development
of a wind farm. Through our model, we show how the values of this quantity can
be obtained both analytically and computationally
First and second order semi-Markov chains for wind speed modeling
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.
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. With the aim of reproducing the statistical properties of this data we
propose the use of three semi-Markov models. We 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
models with those of real data and also with a synthetic time series generated
though a simple Markov chain.Comment: accepted for publication on Physica