# 2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables

arXiv: https://arxiv.org/abs/2505.01286 · HTML: https://mlsanity.com/paper/2505.01286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 2 May 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://mlsanity.com/paper/2402.19072/) (2024)
- [Spatio-Temporal Meta-Graph Learning for Traffic Forecasting](https://mlsanity.com/paper/2211.14701/) (2022)
- [Short-Term Power Prediction for Renewable Energy Using Hybrid Graph Convolutional Network and Long Short-Term Memory Approach](https://mlsanity.com/paper/2111.07958/) (2021)
- [Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space](https://mlsanity.com/paper/1807.05666/) (2018)
- [Deep Spatio-Temporal Wind Power Forecasting](https://mlsanity.com/paper/2109.14530/) (2021)
- [Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer](https://mlsanity.com/paper/2305.18724/) (2023)

## Built on by (most cited first, 1 of 1)

- [UniWind: Day-Ahead Wind Power Forecasting via Physics-Informed State Routing](https://mlsanity.com/paper/2607.01670/) (2026)

## Abstract (arXiv)

Accurate wind power forecasting can help formulate scientific dispatch plans, which is of great significance for maintaining the safety, stability, and efficient operation of the power system. In recent years, wind power forecasting methods based on deep learning have focused on extracting the spatiotemporal correlations among data, achieving significant improvements in forecasting accuracy. However, they exhibit two limitations. First, there is a lack of modeling for the inter-variable relationships, which limits the accuracy of the forecasts. Second, by treating endogenous and exogenous variables equally, it leads to unnecessary interactions between the endogenous and exogenous variables, increasing the complexity of the model. In this paper, we propose the 2DXformer, which, building upon the previous work's focus on spatiotemporal correlations, addresses the aforementioned two limitations. Specifically, we classify the inputs of the model into three types: exogenous static variables, exogenous dynamic variables, and endogenous variables. First, we embed these variables as variable tokens in a channel-independent manner. Then, we use the attention mechanism to capture the correlations among exogenous variables. Finally, we employ a multi-layer perceptron with residual connections to model the impact of exogenous variables on endogenous variables. Experimental results on two real-world large-scale datasets indicate that our proposed 2DXformer can further improve the performance of wind power forecasting. The code is available in this repository: hrefhttps://github.com/jseaj/2DXformerhttps://github.com/jseaj/2DXformer.