Wind power is one of the most efficient renewable resources without emissions. Nonetheless, it is difficult to exactly forecast wind power generation given historical power and wind speed information, the failure of which may cost the risk of large-scale outages. This article takes a close look at the artificial recurrent neural network framework in the application of wind power forecasting. More intelligent mechanisms using attention to capture spatial-temporal patterns within historical data are emphasized in this work and are shown to be state-of-the-art for short-term wind power forecasting. Our experiments at a wind farm in southeast Australia using only the historical wind power generation and wind speed records from ambient weather stations show that, e.g., 7.4750% in mean absolute error (MAE) and 0.3345 in the coefficient of variation in the root mean squared error (CV-RMSE) for half-hour-ahead prediction. To interpret how the three models under consideration-the long-and short-term time-series network (LSTNet), the temporal pattern attention-based long short-term memory (TPA-LSTM) and the dual-stage attention-based recurrent neural network (DA-RNN)-work, we visualize and analyze the details of the models so that further improvement can be made by combining the advantageous components of the models.

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