Prediction of wind blowing durations of Eastern Turkey with machine learning for integration of renewable energy and organic farmingstock raising

Abstract

Applications which integrate wind energy and both agriculture and stock raising are increasingly becoming popular especially in Europe. Subject applications enable the land to be utilized in various favorable ways. In this study, by using a 5-year average wind data referring to Erzurum and Ardahan, two eastern cities of Turkey which are characterized by prevailingly an extensive cattle-raising, wind-blowing durations were calculated by Rayleigh distribution. Annual wind blowing durations for Erzurum and Ardahan ranged between 479.6-5825.7 hours and 1643.6-6710.8 hours, respectively. The data obtained was predicted via artificial neural networks and output results indicate an prediction accuracy at 99% level thereupon. The integration of agricultural and stock raising activities with wind energy shall contribute to environmental aspects as well increasing the efficiency and effectiveness in the region.

Keywords

• Wind energy prediction,wind blowing duration,artificial neural networks,machine learning,organic farming,stock raising

References

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