Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.16591 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912206929526784 |
|---|---|
| author | Song, Hongjin Chen, Qianrun Jiang, Tianqi Li, Yongfeng Li, Xusheng Xi, Wenjun Huang, Songtao |
| author_facet | Song, Hongjin Chen, Qianrun Jiang, Tianqi Li, Yongfeng Li, Xusheng Xi, Wenjun Huang, Songtao |
| contents | Accurately predicting the wind power output of a wind farm across various time scales utilizing Wind Power Forecasting (WPF) is a critical issue in wind power trading and utilization. The WPF problem remains unresolved due to numerous influencing variables, such as wind speed, temperature, latitude, and longitude. Furthermore, achieving high prediction accuracy is crucial for maintaining electric grid stability and ensuring supply security. In this paper, we model all wind turbines within a wind farm as graph nodes in a graph built by their geographical locations. Accordingly, we propose an ensemble model based on graph neural networks and reinforcement learning (EMGRL) for WPF. Our approach includes: (1) applying graph neural networks to capture the time-series data from neighboring wind farms relevant to the target wind farm; (2) establishing a general state embedding that integrates the target wind farm's data with the historical performance of base models on the target wind farm; (3) ensembling and leveraging the advantages of all base models through an actor-critic reinforcement learning framework for WPF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting Song, Hongjin Chen, Qianrun Jiang, Tianqi Li, Yongfeng Li, Xusheng Xi, Wenjun Huang, Songtao Machine Learning Artificial Intelligence Accurately predicting the wind power output of a wind farm across various time scales utilizing Wind Power Forecasting (WPF) is a critical issue in wind power trading and utilization. The WPF problem remains unresolved due to numerous influencing variables, such as wind speed, temperature, latitude, and longitude. Furthermore, achieving high prediction accuracy is crucial for maintaining electric grid stability and ensuring supply security. In this paper, we model all wind turbines within a wind farm as graph nodes in a graph built by their geographical locations. Accordingly, we propose an ensemble model based on graph neural networks and reinforcement learning (EMGRL) for WPF. Our approach includes: (1) applying graph neural networks to capture the time-series data from neighboring wind farms relevant to the target wind farm; (2) establishing a general state embedding that integrates the target wind farm's data with the historical performance of base models on the target wind farm; (3) ensembling and leveraging the advantages of all base models through an actor-critic reinforcement learning framework for WPF. |
| title | Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.16591 |