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Wind Speed Prediction of Four Regions in Northern Cyprus Prediction Using ARIMA and Artificial Neural Networks Models: A Comparison Study

  • Youssef Kassem
  • Hüseyin Gökçekuş
  • Hüseyin Çamur
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

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.

Keywords

ARIMA ANN Northern cyprus Wind speed 

Notes

Acknowledgments

The authors would like to thank the Faculty of Civil and Environmental Engineering especially the Civil Engineering Department.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Youssef Kassem
    • 1
    • 2
  • Hüseyin Gökçekuş
    • 1
  • Hüseyin Çamur
    • 2
  1. 1.Faculty of Civil and Environmental EngineeringNear East UniversityNicosiaTurkey
  2. 2.Faculty of Engineering, Mechanical Engineering DepartmentNear East UniversityNicosiaTurkey

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