Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks. A case study
DOI:
https://doi.org/10.24084/repqj09.595Abstract
The economic feasibility of a wind project is dependent on the
wind regime since it relies on the power output of the turbines
over the installation’s working life. Consequently, the
interannual variability of wind speed at a potential wind energy
conversion site is an issue of capital importance.
Usually a wind data measurement campaign is limited to a
period no longer than one year (i.e. short-term). Therefore, the
process of decision-making for wind farm constructors must be
based in this short-term data.
Various methods have been proposed in the scientific literature
for estimation of the long-term wind speed characteristics at
such sites. These methods use simultaneous measurements of
the wind speed at the site in question and at one or several
nearby reference sites with a long history of wind data
measurements.
In this paper, long-term wind power densities which have been
estimated through the use Artificial Neural Networks (ANNs),
will be compared to those which have been calculated by means
of the short-term wind data (i.e. considered to be representative
of long-term wind performance).
Mean hourly wind speeds and directions calculated in a 10 year
period of time at six weather stations located on six different
islands in the Canarian Archipelago (Spain) were used in this
study.
Among the different conclusions which this study revealed, we
can highlight that the wind resource estimation based on ANNs
is better than that dependant on short-term wind data. This is
true when the correlation coefficient between the reference and
candidate weather station is of 0.6.