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

Colak, IlhamiSagiroglu, SerefYesilbudak, MehmetKabalci, ErsanIbrahim Bulbul, H.2024-09-112024-09-112015978-147999982-8https://doi.org/10.1109/ICRERA.2015.7418697https://hdl.handle.net/11363/86114th International Conference on Renewable Energy Research and Applications, ICRERA 2015 -- 22 November 2015 through 25 November 2015 -- Palermo -- 119791This 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. © 2015 IEEE.eninfo:eu-repo/semantics/closedAccessforecasting; short-term; Statistical methods; very short-term; wind power; wind speedMulti-time series and-time scale modeling for wind speed and wind power forecasting part I: Statistical methods, very short-term and short-term applicationsConference Object20921410.1109/ICRERA.2015.74186972-s2.0-84964623345N/A