# SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022

arXiv: https://arxiv.org/abs/2208.04360 · HTML: https://mlsanity.com/paper/2208.04360/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 8 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network](https://mlsanity.com/paper/1905.13290/) (2019)
- [A Collection and Categorization of Open-Source Wind and Wind Power Datasets](https://mlsanity.com/paper/2202.08524/) (2022)
- [Deep Spatio-Temporal Wind Power Forecasting](https://mlsanity.com/paper/2109.14530/) (2021)

## Built on by (most cited first, 21 of 21)

- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [Timer: Generative Pre-trained Transformers Are Large Time Series Models](https://mlsanity.com/paper/2402.02368/) (2024)
- [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://mlsanity.com/paper/2311.01933/) (2023)
- [TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation](https://mlsanity.com/paper/2410.05711/) (2024)
- [Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling](https://mlsanity.com/paper/2605.27286/) (2026)
- [TimeFound: A Foundation Model for Time Series Forecasting](https://mlsanity.com/paper/2503.04118/) (2025)
- [AutoML for Climate Change: A Call to Action](https://mlsanity.com/paper/2210.03324/) (2022)
- [BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022](https://mlsanity.com/paper/2302.11159/) (2023)
- [Into the ORBIT for Time Series: Training Regimes for Foundation Models](https://mlsanity.com/paper/2608.13262/) (2026)
- [Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022](https://mlsanity.com/paper/2307.09248/) (2023)
- [FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting](https://mlsanity.com/paper/2604.22328/) (2026)
- [WPBench: A Comprehensive Benchmark for Wind Power Forecasting](https://mlsanity.com/paper/2609.24444/) (2026)
- [KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution](https://mlsanity.com/paper/2208.08952/) (2022)
- [ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting](https://mlsanity.com/paper/2412.14537/) (2024)
- [Unified machine learning tasks and datasets for enhancing renewable energy](https://mlsanity.com/paper/2311.06876/) (2023)
- [Hiformer: Hybrid Frequency Feature Enhancement Inverted Transformer for Long-Term Wind Power Prediction](https://mlsanity.com/paper/2410.13303/) (2024)
- [CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting](https://mlsanity.com/paper/2602.03564/) (2026)
- [Modern Structure-Aware Simplicial Spatiotemporal Neural Network](https://mlsanity.com/paper/2604.15833/) (2026)
- [Reversible Residual Normalization Alleviates Spatio-Temporal Distribution Shift](https://mlsanity.com/paper/2604.15838/) (2026)
- [LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications](https://mlsanity.com/paper/2607.18147/) (2026)

## Abstract (arXiv)

The variability of wind power supply can present substantial challenges to incorporating wind power into a grid system. Thus, Wind Power Forecasting (WPF) has been widely recognized as one of the most critical issues in wind power integration and operation. There has been an explosion of studies on wind power forecasting problems in the past decades. Nevertheless, how to well handle the WPF problem is still challenging, since high prediction accuracy is always demanded to ensure grid stability and security of supply. We present a unique Spatial Dynamic Wind Power Forecasting dataset: SDWPF, which includes the spatial distribution of wind turbines, as well as the dynamic context factors. Whereas, most of the existing datasets have only a small number of wind turbines without knowing the locations and context information of wind turbines at a fine-grained time scale. By contrast, SDWPF provides the wind power data of 134 wind turbines from a wind farm over half a year with their relative positions and internal statuses. We use this dataset to launch the Baidu KDD Cup 2022 to examine the limit of current WPF solutions. The dataset is released at https://aistudio.baidu.com/aistudio/competition/detail/152/0/datasets.