Estimation of wind speed with artificial neural networks method for Isparta using meteorological measurement data

Yıl 2021, , 65 - 69, 30.06.2021

Fatih Gemici , Arzu Şencan Şahin

https://doi.org/10.31593/ijeat.895362

Cited By: 1

Öz

Renewable energy sources are of great importance for our country. Wind energy is a renewable energy source. Today, wind energy is mostly used in electricity generation. The electrical energy to be produced from a wind turbine is directly related to the wind speed in that region.
In this study, the wind speed for Isparta between 1st of January 2019, and 31st of December 2019 has been estimated using an artificial neural network (ANN) depending on average air temperature (°C), air pressure (mb), relative humidity (%), solar radiation (W/m2). MATLAB programming language is used. RMSE (Root-Mean-Square Error) was found to be 6,946427364, and R2 value as 0.9479, cov coefficient of variation as 0.1609336. It has been observed that these values are at an acceptable level. Therefore, it has been seen that the artificial neural networks model can be used in wind speed estimation.

Anahtar Kelimeler

Artificial neural network, Matlab, Wind speed, Prediction

Kaynakça

• https://www.enerjiportali.com/ruzgar-enerjisi-nedir/ (10 January 2021.)
• ilgili, M., Sahin, B., Yasar, A. 2007. Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy, 32(14), 2350-2360.
• Liu, H., Tian, H. Q., Li, Y. F. 2012. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Applied Energy, 98, 415-424.
• Kani, S. P., Ardehali, M. M. 2011. Very short-term wind speed prediction: A new artificial neural network–Markov chain model. Energy Conversion and Management, 52(1), 738-745.
• Liu, H., Chen, C., Tian, H. Q., Li, Y. F. 2012. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renewable energy, 48, 545-556.
• Sheela, K. G., Deepa, S. N. 2013. Neural network based hybrid computing model for wind speed prediction. Neurocomputing, 122, 425-429.
• u, Q., Zhang, R., Zhou, Y. 2016. Transfer learning for short-term wind speed prediction with deep neural networks. Renewable Energy, 85, 83-95.
• Filik, Ü. B., Filik, T. 2017. Wind speed prediction using artificial neural networks based on multiple local measurements in Eskisehir. Energy Procedia, 107, 264-269.
• Ramasamy, P., Chandel, S. S., Yadav, A. K. 2015. Wind speed prediction in the mountainous region of India using an artificial neural network model. Renewable Energy, 80, 338-347.
• Monfared, M., Rastegar, H., Kojabadi, H. M. 2009. A new strategy for wind speed forecasting using artificial intelligent methods. Renewable energy, 34(3), 845-848.
• Kırbaş, İ. 2018. İstatistiksel metotlar ve yapay sinir ağları kullanarak kısa dönem çok adımlı rüzgâr hızı tahmini. Sakarya University Journal of Science, 22(1), 24-38.
• Dikmen, E., Örgen, F. K. 2018. Ağlasun Bölgesi İçin Rüzgâr Hızı Tahmini ve En Uygun Türbin Tespiti. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 7(2), 871-879.
• Köse, B., Atila, Ü., Güneşer, M. T., Recebli, Z. 2016. An Approach to Estimate Hourly & Daily Mean Wind Speed and Comparison with Artificial Neural Network. In 10th International Clean Energy Symposium (pp. 24-26).
• Kılıç, B., Arabacı, E. 2015. Burdur İli Gelecekteki Rüzgâr Hızı Değerlerinin Yapay Sinir Ağları Metodu ile Tahmini. Dumlupınar Üniversitesi Fen Bilimleri Enstitüsü Dergisi, (2015 Özel Sayısı), 45-50.
• Üneş, F., Kasal, D., Taşar, B. 2019. Meterolojik Ölçüm Verilerini Kullanarak Mamdani-Bulanık Mantık Yöntemi ile Rüzgar Hızının Tahmini. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2(1), 97-104.
• Akarslan, E., Hocaoğlu, F. O. 2019. Rüzgar Hızı Verilerinin Yeni Bir Yaklaşım ile Modellenmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 19(1), 121-128.
• Altınsoy, M., Bal, G. 2019. Uzun dönem rüzgâr hizi tahmininde yapay sinir ağlarinin kullanimi ve performans İncelemesi. Mesleki Bilimler Dergisi (MBD), 8(1), 21-28.
• Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R., Basaran, M. A. 2009. A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Systems with Applications, 36(7), 10589-10594.
• Öztopal, A., Şen, Z. 2011. Kısa vadeli yağış modellemesi için yapay sinir ağları yaklaşımı. İTÜDERGİSİ/d, 8(1).
• Öztemel, E. 2003. Yapay sinir ağlari. PapatyaYayincilik, Istanbul.
• Suzuki, K. (Ed.). 2011. Artificial neural networks: methodological advances and biomedical applications. BoD–Books on Demand.
• Bechtler, H., Browne, M. W., Bansal, P. K., Kecman, V. 2001. New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks. Applied Thermal Engineering, 21(9), 941-953.