融合天气信息与高维场景生成，提升风电光伏预测精度
Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
- 用天气变量增强时序模型，恢复时空相关性
- 基于高斯耦合生成真实电力场景，准确率显著提升
- 适合能源系统规划与调度人员参考
可再生能源（RES）接入电网带来固有的随机性和不确定性，亟需发展可靠高效的预测技术。本文提出一种结合概率预测与高斯耦合的方法，用于高维场景下的负荷、风力与太阳能发电的日前预测与场景生成。通过引入天气协变量并恢复时空相关性，所提方法提升了可再生能源预测的可靠性。在中西部独立系统运营商（MISO）的真实高维数据集上，通过多种时间序列模型对比实验，使用全面指标评估性能。结果表明天气信息至关重要，且高斯耦合能有效生成真实场景；所提出的天气感知时序融合变换器（WI-TFT）模型表现最优。
原文摘要 · Abstract (English)
The integration of renewable energy sources (RES) into power grids presents significant challenges due to their intrinsic stochasticity and uncertainty, necessitating the development of new techniques for reliable and efficient forecasting. This paper proposes a method combining probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of load, wind, and solar power in high-dimensional contexts. By incorporating weather covariates and restoring spatio-temporal correlations, the proposed method enhances the reliability of probabilistic forecasts in RES. Extensive numerical experiments compare the effectiveness of different time series models, with performance evaluated using comprehensive metrics on a real-world and high-dimensional dataset from Midcontinent Independent System Operator (MISO). The results highlight the importance of weather information and demonstrate the efficacy of the Gaussian copula in generating realistic scenarios, with the proposed weather-informed Temporal Fusion Transformer (WI-TFT) model showing superior performance.
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