Abstract
References
- [1] Yussefi, M., Willer, H. (2003). The World of Organic Agriculture Statistics and Future Prospects Annual Report, p. 3-16.
- [2] Kırbaş İ. (2018). Short-term multi-step wind speed prediction using statistical methods and artificial neural networks. Sakarya University Journal Of Science, vol. 22, no. 1, p. 24-38. (in Turkish)
- [3] Ata R. (2014). Neural Predictıon of Wind Blowing Durations Based on Average Wind Speeds for Akhisar Location, Pamukkale University Journal of Engineering Sciences, vol. 20, no. 5, p. 162-165. (in Turkish)
- [4] Gnatowska, R., Moryn-Kucharczyk, E. (2019), Current status of wind energy policy in Poland. Renewable Energy, vol. 135, p. 232-237.
- [5] Yan, J., Ouyang, T. (2019). Advanced wind power prediction based on data-driven error correction. Energy Conversion and Management, vol. 180, p. 302–311.
- [6] Güngör A., Eskin N. (2008). The Characteristics That Define Wind as an Energy Source. Energy Sources, Part A: Recovery, Utilization, vol. 30, p. 842-855.
- [7] Güngör A. (2015). Effects of global warming on wind energy potential, World Journal of Engineering, vol. 12, p. 369-374.
- [8] Turkish General Directorate of Meterological Affairs, Official letter no. 8059, 17.07.2019.
- [9] Koçyiğit, R., Aydın, R., Diler, A. (2015). Situation of the Cattle Production in Erzurum Province and Some Suggestions for Its Improvement. Alınteri Journal of Agriculture Science, vol. 29, no. 2, p. 34-46. (in Turkish)
- [10] Uçum, İ., Gülçubuk, B. (2018). Local Industrial Enterprises Based on Livestock Production and Problems in the Process of Contribution to the Local Economy. KSU J. Agric Nat, vol. 21(Special Issue), p. 44-54. (in Turkish)
- [11] Aygün, G., Akbulak, C. (2017). Evaluation of the Organic Livestock Potential of Ardahan Province. Dumlupınar University Journal of Social Sciences, vol. 53, p. 144-161. (in Turkish)
- [12] Akpınar K.E., Balpetek N., (2018). Statistical analysis of wind energy potential of Elazığ province according to Weibull and Rayleigh distributions, Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 18, no. 1. (in Turkish)
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
- [1] Yussefi, M., Willer, H. (2003). The World of Organic Agriculture Statistics and Future Prospects Annual Report, p. 3-16.
- [2] Kırbaş İ. (2018). Short-term multi-step wind speed prediction using statistical methods and artificial neural networks. Sakarya University Journal Of Science, vol. 22, no. 1, p. 24-38. (in Turkish)
- [3] Ata R. (2014). Neural Predictıon of Wind Blowing Durations Based on Average Wind Speeds for Akhisar Location, Pamukkale University Journal of Engineering Sciences, vol. 20, no. 5, p. 162-165. (in Turkish)
- [4] Gnatowska, R., Moryn-Kucharczyk, E. (2019), Current status of wind energy policy in Poland. Renewable Energy, vol. 135, p. 232-237.
- [5] Yan, J., Ouyang, T. (2019). Advanced wind power prediction based on data-driven error correction. Energy Conversion and Management, vol. 180, p. 302–311.
- [6] Güngör A., Eskin N. (2008). The Characteristics That Define Wind as an Energy Source. Energy Sources, Part A: Recovery, Utilization, vol. 30, p. 842-855.
- [7] Güngör A. (2015). Effects of global warming on wind energy potential, World Journal of Engineering, vol. 12, p. 369-374.
- [8] Turkish General Directorate of Meterological Affairs, Official letter no. 8059, 17.07.2019.
- [9] Koçyiğit, R., Aydın, R., Diler, A. (2015). Situation of the Cattle Production in Erzurum Province and Some Suggestions for Its Improvement. Alınteri Journal of Agriculture Science, vol. 29, no. 2, p. 34-46. (in Turkish)
- [10] Uçum, İ., Gülçubuk, B. (2018). Local Industrial Enterprises Based on Livestock Production and Problems in the Process of Contribution to the Local Economy. KSU J. Agric Nat, vol. 21(Special Issue), p. 44-54. (in Turkish)
- [11] Aygün, G., Akbulak, C. (2017). Evaluation of the Organic Livestock Potential of Ardahan Province. Dumlupınar University Journal of Social Sciences, vol. 53, p. 144-161. (in Turkish)
- [12] Akpınar K.E., Balpetek N., (2018). Statistical analysis of wind energy potential of Elazığ province according to Weibull and Rayleigh distributions, Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 18, no. 1. (in Turkish)
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Original Research Articles |
| Authors | |
| Publication Date | September 30, 2019 |
| Acceptance Date | September 17, 2019 |
| Published in Issue | Year 2019 Volume: 2 Issue: 3 |
