  Wind power forecasting helps with the planning for the power systems by
contributing to having a higher level of certainty in decision-making. Due to
the randomness inherent to meteorological events (e.g., wind speeds), making
highly accurate long-term predictions for wind power can be extremely
difficult. One approach to remedy this challenge is to utilize weather
information from multiple points across a geographical grid to obtain a
holistic view of the wind patterns, along with temporal information from the
previous power outputs of the wind farms. Our proposed CNN-RNN architecture
combines convolutional neural networks (CNNs) and recurrent neural networks
(RNNs) to extract spatial and temporal information from multi-dimensional input
data to make day-ahead predictions. In this regard, our method incorporates an
ultra-wide learning view, combining data from multiple numerical weather
prediction models, wind farms, and geographical locations. Additionally, we
experiment with global forecasting approaches to understand the impact of
training the same model over the datasets obtained from multiple different wind
farms, and we employ a method where spatial information extracted from
convolutional layers is passed to a tree ensemble (e.g., Light Gradient
Boosting Machine (LGBM)) instead of fully connected layers. The results show
that our proposed CNN-RNN architecture outperforms other models such as LGBM,
Extra Tree regressor and linear regression when trained globally, but fails to
replicate such performance when trained individually on each farm. We also
observe that passing the spatial information from CNN to LGBM improves its
performance, providing further evidence of CNN's spatial feature extraction
capabilities.
