Abstract
Wind speed data is one of the most critical factors affecting the operation of wind power farm systems. This paper examines the forecasting performance of Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models for predicting wind speeds in four regions in Northern Cyprus: Lefkoşa, Girne, Salamis, and Boğaz. For the application of the methodology, the meteorological measurements including wind speed, air temperature, humidity, sunshine duration, global solar radiation and rainfall values, from 1 January 2013 to 31 December 2016, were used. The obtained results demonstrated that the ANN model realizes the best accuracy for the prediction of the wind speeds with the highest R-squared value.
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The authors would like to thank the Faculty of Civil and Environmental Engineering especially the Civil Engineering Department.
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Kassem, Y., Gökçekuş, H., Çamur, H. (2019). Wind Speed Prediction of Four Regions in Northern Cyprus Prediction Using ARIMA and Artificial Neural Networks Models: A Comparison Study. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_32
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DOI: https://doi.org/10.1007/978-3-030-04164-9_32
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