Yazar "Yeşilbudak, Mehmet" seçeneğine göre listele

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  • Küçük Resim
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    Multi-Period Prediction of Solar Radiation Using ARMA and ARIMA Models
    (ELSEVIER SCIENCE BV, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, 2015-12-09) Çolak, İlhami; Yeşilbudak, Mehmet; Genç, Naci; Bayındır, Ramazan
    Due to the variations in weather conditions, solar power integration to the electricity grid at a high penetration rate can cause a threat for the grid stability. Therefore, it is required to predict the solar radiation parameter in order to ensure the quality and the security of the grid. In this study, initially, a 1-h time series model belong to the solar radiation parameter is created for multi-period predictions. Afterwards, autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are compared in terms of the goodness-of-fit value produced by the log-likelihood function. As a result of determining the best statistical models in multi-period predictions, one-period, two-period and three-period ahead predictions are carried out for the solar radiation parameter in a comprehensive way. Many feasible comparisons have been made for the solar radiation prediction.
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    Multi-Time Series and -Time Scale Modeling for Wind Speed and Wind Power Forecasting Part I: Statistical Methods, Very Short-Term and Short-Term Applications
    (IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2015) Çolak, İlhami; Sağıroğlu, Şeref; Yeşilbudak, Mehmet; Kabalcı, Ersan; Bülbül, H. İbrahim
    This study concentrates on multi-time series and time scale modeling in wind speed and wind power forecasting. Different statistical models along with different time horizons are analyzed and evaluated broadly and comprehensively. For this reason, the entire study is divided into two main scientific parts. In case of making a general overview of the entire study, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) methods are employed for multi-time series modeling. Very short-term, short-term, medium-term and long-term scales are utilized for multi-time scale modeling. Specifically, in this part of the entire study, the mentioned statistical models are presented in detail and 10-min and 1-h time series forecasting models are created for the purpose of achieving 10-min and 2-h ahead forecasting, respectively. Many useful outcomes are accomplished for very short-term and short-term wind speed and wind power forecasting.
  • Küçük Resim
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    Multi-Time Series and -Time Scale Modeling for Wind Speed and Wind Power Forecasting Part II: Medium-Term and Long-Term Applications
    (IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2015) Çolak, İlhami; Sağıroğlu, Şeref; Yeşilbudak, Mehmet; Kabalcı, Ersan; Bülbül, H. İbrahim
    This paper represents the second part of an entire study which focuses on multi-time series and -time scale modeling in wind speed and wind power forecasting. In the first part of the entire study [1], firstly, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are introduced in-depth. Afterwards, the mentioned models are analyzed for very short-term and short-term forecasting scales, comprehensively. In this second part of the entire study, we address the medium-term and long-term prediction performance of MA, WMA, ARMA and ARIMA models in wind speed and wind power forecasting. Particularly, 3-h and 6-h time series forecasting models are constructed in order to carry out 9-h and 24-h ahead forecasting, respectively. Many valuable assessments are made for the employed statistical models in terms of medium-term and long-terms forecasting scales. Finally, many valuable achievements are discussed considering a detailed comparison chart of the entire study.