  With the increased penetration of wind energy into the power grid, it has
become increasingly important to be able to predict the expected power
production for larger wind farms. Deep learning (DL) models can learn complex
patterns in the data and have found wide success in predicting wake losses and
expected power production. This paper proposes a modular framework for
attention-based graph neural networks (GNN), where attention can be applied to
any desired component of a graph block. The results show that the model
significantly outperforms a multilayer perceptron (MLP) and a bidirectional
LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN
model. Moreover, we argue that the proposed graph attention architecture can
easily adapt to different applications by offering flexibility into the desired
attention operations to be used, which might depend on the specific
application. Through analysis of the attention weights, it was showed that
employing attention-based GNNs can provide insights into what the models learn.
In particular, the attention networks seemed to realise turbine dependencies
that aligned with some physical intuition about wake losses.
