# Deep Spatio-Temporal Wind Power Forecasting

arXiv: https://arxiv.org/abs/2109.14530 · HTML: https://mlsanity.com/paper/2109.14530/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting](https://mlsanity.com/paper/1707.08110/) (2017)
- [Spatiotemporal Attention Networks for Wind Power Forecasting](https://mlsanity.com/paper/1909.07369/) (2019)

## Built on by (most cited first, 3 of 3)

- [SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022](https://mlsanity.com/paper/2208.04360/) (2022)
- [Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022](https://mlsanity.com/paper/2307.09248/) (2023)
- [2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables](https://mlsanity.com/paper/2505.01286/) (2025)

## Abstract (arXiv)

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.