Journal
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY
Volume 38, Issue 6, Pages 526-541Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/14786451.2018.1516668
Keywords
Renewable energy; artificial neural networks; wind speed prediction; wind energy
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The present study targets short-term wind speed prediction of the wind turbine station at Bogdanci in the Former Yugoslav Republic of Macedonia (FYROM), using artificial neural network (ANN) method. Wind directions and meteorological parameters (temperature, pressure, and humidity) measured at the interval of 10 min in between May 2015 and September 2015 have been used as the input of ANN to predict four kinds of wind speed (rotation mean, hub mean, tip low mean, and base mean). The best performance (R-2 = 0.84 - 0.86) of ANN method was achieved using wind direction base mean (WDBM) in September 2015, and using temperature (R-2 = 0.77 - 0.80) in May 2015. Reasonable performance of ANN method was achieved in the rest of the month.
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