Scholarly record
LONG TERM PREDICTION OF WIND SPEED WITH ARTIFICIAL NEURAL NETWORKS
Abstract
The paper presents a methodology, based on artificial neural networks, for the offshore wind speed values long-term prediction. One of the most available renewable energy is the wind energy. The offshore wind turbines placement presents some benefits comparing with onshore one, like lower environmental impact and long-term stability of surroundings geometry. Of course, there are also drawbacks, like difficult set-up and maintenance or expensive transmission systems for the converted energy. Over all, the off-shore placement of wind turbines is continuously growing. Taking into account that the wind turbines designing and mounting is an expensive job, the prediction of wind speed value evolution on long-term basis is a must for an efficient investment. Due to the present-day changes of the weather conditions, the estimation of wind speed long-term evolution using mathematical equations is difficult to obtain. On the other hand, there are available large databases with recorded values of wind speed over the years. Based on artificial neural networks, these datasets can be processed and used for long-term predictions, allowing this way a more efficient wind energy extraction in offshore facilities. In the present paper, the values of wind speed recorded in some locations, were processed with an artificial neural network, and some predictions were made. In order to validate the procedure, the obtained results were compared with corresponding measured values. Some long-term predictions are also presented.
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References6
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Faniband Y.P., Shaahid S.M., Forecasting wind speed using artificial neural networks - a case study of a potential location of Saudi Arabia, 5th International Conference on Advances on Clean Energy Research ICACER 2020, Spain, 2020
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Number of times cited according to Crossref: 1
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