  Wind power forecasting has drawn increasing attention among researchers as
the consumption of renewable energy grows. In this paper, we develop a deep
learning approach based on encoder-decoder structure. Our model forecasts wind
power generated by a wind turbine using its spatial location relative to other
turbines and historical wind speed data. In this way, we effectively integrate
spatial dependency and temporal trends to make turbine-specific predictions.
The advantages of our method over existing work can be summarized as 1) it
directly predicts wind power based on historical wind speed, without the need
for prediction of wind speed first, and then using a transformation; 2) it can
effectively capture long-term dependency 3) our model is more scalable and
efficient compared with other deep learning based methods. We demonstrate the
efficacy of our model on the benchmark real-world datasets.
