# Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses

arXiv: https://arxiv.org/abs/2201.03229 · HTML: https://mlsanity.com/paper/2201.03229/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Attention Models in Graphs: A Survey](https://mlsanity.com/paper/1807.07984/) (2018)
- [Deep Graph Convolutional Networks for Wind Speed Prediction](https://mlsanity.com/paper/2101.10041/) (2021)
- [Edge-Featured Graph Attention Network](https://mlsanity.com/paper/2101.07671/) (2021)
- [Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting](https://mlsanity.com/paper/1707.08110/) (2017)
- [Edge Attention-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1802.04944/) (2018)
- [Spatiotemporal Attention Networks for Wind Power Forecasting](https://mlsanity.com/paper/1909.07369/) (2019)

## Built on by (most cited first, 2 of 2)

- [BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022](https://mlsanity.com/paper/2302.11159/) (2023)
- [Learning to Optimise Wind Farms with Graph Transformers](https://mlsanity.com/paper/2311.12750/) (2023)

## Abstract (arXiv)

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.