Wind Speed Prediction of Four Regions in Northern Cyprus Prediction Using ARIMA and Artificial Neural Networks Models: A Comparison Study
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.
KeywordsARIMA ANN Northern cyprus Wind speed
The authors would like to thank the Faculty of Civil and Environmental Engineering especially the Civil Engineering Department.
- 6.Aquino, R.R., Gouveia, H.T., Lira, M.M., Ferreira, A.A., Neto, O.N., Carvalho, M.A.: Models based on neural networks and neuro-fuzzy systems for wind power prediction using wavelet transform as data preprocessing method. In: Engineering Applications of Neural Networks Communications in Computer and Information Science, pp. 272–281 (2012)Google Scholar
- 8.Litta, A.J., Idicula, S.M., Mohanty, U.C.: Artificial neural network model in prediction of meteorological parameters during premonsoon thunderstorms. Int. J. Atmos. Sci. 2013, 1–14 (2013)Google Scholar