用图神经网络和强化学习融合多风力机数据，提升风电预测精度。
Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting
- 将风场风机建模为地理图节点，捕捉邻近风场时间序列相关性。
- 通过状态嵌入融合历史表现，动态加权多个基础模型。
- 基于强化学习框架实现模型集成，显著提升预测准确性。
准确预测风场在不同时间尺度下的风电输出是风电交易与利用的关键问题。由于风速、温度、经纬度等多重因素影响，风电预测难题仍未解决。为保障电网稳定与供电安全，高精度预测至关重要。本文将风场内所有风力机视为由地理位置构建的图中的节点，提出一种基于图神经网络与强化学习的集成模型（EMGRL）。该方法包括：(1) 利用图神经网络捕捉与目标风场相关的邻近风场的时间序列数据；(2) 构建包含目标风场数据及基模型历史表现的状态嵌入；(3) 通过演员-评论家强化学习框架对所有基模型进行集成，充分发挥其优势。实验验证了该方法在多时间尺度上的有效性。
原文摘要 · Abstract (English)
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.
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