[abstFig src='/00280005/09.jpg' width='300' text='Prediction errors at observation points' ] Wind energy use is being developed worldwide. Improving wind speed forecasting techniques has become important due to their economic impact on power system operation with increasing wind power penetration. Wind speed prediction is generally difficult due to wind’s intermittent nature, so many approaches have been proposed by researchers. The viability of these techniques has been verified, however, in only a certain few areas, rather than being evaluated quantitatively in many different locations. We use data from different parts of Japan for one-step-ahead prediction and applied different approaches at each point, which was then evaluated such as mean absolute error. We used the persistent model, the ARMA-GARCH model, the nonlinear autoregressive network with external input (NARX), the recurrent neural network (RNN), and support vector regression (SVR). Our results suggest that it is difficult to create the same model which minimizes error in all areas, confirming the need for individual predictors for individual regions.
When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages
