Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 15Citation - Scopus: 18Extreme Value Statistics of Wind Speed and Wave Height of the Marmara Sea Based on Combined Radar Altimeter Data(Elsevier, 2019) Özbahçeci, BergüzarBoth reliable and long-term wind and wave data are necessary for the design of coastal and offshore structures. Due to lack of sufficient in-situ measurement data, modeling data have been used in the limited number of wind and wave climate studies of the Marmara Sea. Satellites equipped with instruments capable of observing marine surface wind and ocean waves like Radar Altimeter can be another source for the long term wind and wave climate of the Marmara Sea. In this study, for the first time, the altimeter wind speed and the significant wave height data from different satellite missions are attempted to use in the climate and extreme value analysis of the Marmara Sea. Altimeter wind speeds and significant wave heights are compared with the in-situ measurements and it is found that while the altimeter wind speed agrees with the measurement data, the significant wave height data should be calibrated. After the calibration of the altimeter data and the inter-calibrations of earlier satellite missions, 27 years of altimeter wind speed and wave height data are obtained to use in extreme value analysis. The wind speed and the significant wave height values corresponding to various return periods are determined as a result of extreme value statistics and those values are compared with the results of the measurements and previous studies. Consistent extreme values computed in the current study indicate that the combined radar altimeter data can be used in the wind and the wave climate calculations and the extreme value analysis of the Marmara Sea. © 2019 COSPARArticle Citation - WoS: 103Citation - Scopus: 122One-Day Ahead Wind Speed/Power Prediction Based on Polynomial Autoregressive Model(Institution of Engineering and Technology, 2017) Karakuş, Oktay; Kuruoğlu, Ercan Engin; Altınkaya, Mustafa AzizWind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Ceşme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h.