Comparison between the short-term observed and long-term estimated wind power density using Artificial Neural Networks. A case study

Authors

  • S Velázquez Author
  • JA. Carta Author

DOI:

https://doi.org/10.24084/repqj09.595

Abstract

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.

Author Biographies

  • S Velázquez

    Department of Electronics and Automatics Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n,

    35017 Las Palmas de Gran Canaria, Canary Islands (Spain).

    Tel.: +34 928 45 96 71, Fax: +34 928 45 73 19 .E-mail address: svelazquez@diea.ulpgc.es

  • JA. Carta

    Department of Mechanical Engineering, University of Las Palmas de Gran Canaria,

    Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands ( Spain).

    Tel.: +34 928 45 10 00, Fax: +34 928 45 14 83 .E-mail address: jcarta@dim.ulpgc.es

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Published

2024-01-17

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Articles