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- W2908314790 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: Lefkosa, Girne, Salamis, and Bogaz. 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." @default.
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- W2908314790 date "2018-12-29" @default.
- W2908314790 modified "2023-09-25" @default.
- W2908314790 title "Wind Speed Prediction of Four Regions in Northern Cyprus Prediction Using ARIMA and Artificial Neural Networks Models: A Comparison Study" @default.
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- W2908314790 doi "https://doi.org/10.1007/978-3-030-04164-9_32" @default.
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