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http://dx.doi.org/10.5351/KJAS.2016.29.7.1373

A study on short-term wind power forecasting using time series models  

Park, Soo-Hyun (Department of Applied Statistics, Chung-Ang University)
Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.7, 2016 , pp. 1373-1383 More about this Journal
Abstract
The wind energy industry and wind power generation have increased; consequently, the stable supply of the wind power has become an important issue. It is important to accurately predict the wind power with short-term basis in order to make a reliable planning for the power supply and demand of wind power. In this paper, we first analyzed the speed, power and the directions of the wind. The neural network and the time series models (ARMA, ARMAX, ARMA-GARCH, Holt Winters) for wind power generation forecasting were compared based on mean absolute error (MAE). For one to three hour-ahead forecast, ARMA-GARCH model was outperformed, and the neural network method showed a better performance in the six hour-ahead forecast.
Keywords
wind power; neural network; ARMAX; ARMA GARCH; Holt Winters;
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