Forecast of Hourly Average Wind Speed Using ARMA Model with Discrete Probability Transformation

In this paper the methodology for wind speed forecasting with ARMA model is revised. The transformation, standardization, estimation and diagnostic checking processes are analyzed and a discrete probability transformation is introduced. Using time series historical data of three weather stations of the Royal Netherlands Meteorological Institute, the forecasting accuracy is evaluated for prediction intervals between 1 and 10 hours ahead and compared with artificial neural network training by back-propagation algorithm (BP-ANN). The results show that for the wind speed time series under study, in certain cases the ARMA model with discrete probability transformation can improve the BP-ANN at least 17.71%.

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