建模可再生能源的动态时间相关性，提升短期场景生成精度。
A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation
- 分离建模边缘分布与相关结构，用评分规则保证合理性。
- 动态协方差网络捕捉时变相关性，提升黑箱模型可解释性。
- 非参数连续分位数建模，支持逆采样生成真实场景。
可再生能源受大气系统影响，具有非线性和时变特性。为此，提出一种面向可再生能源情景生成的动态时间相关性建模框架。采用新型解耦映射路径联合建模边缘分布与相关结构，利用合适的评分规则对边际分布和相关结构分别进行回归，确保建模过程合理性。情景生成分为两个阶段：首先，动态相关网络基于动态协方差矩阵建模时间相关性，捕捉可再生能源的时变特征，增强黑箱模型的可解释性；其次，隐式分位数网络以非参数、连续方式建模边际分位数函数，通过边际逆采样实现情景生成。实验结果表明，所提出的动态相关分位数网络在量化不确定性与捕捉动态相关性方面优于当前最优方法，适用于短期可再生能源情景生成。
原文摘要 · Abstract (English)
Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for renewable energy scenario generation. A novel decoupled mapping path is employed for joint probability distribution modeling, formulating regression tasks for both marginal distributions and the correlation structure using proper scoring rules to ensure the rationality of the modeling process. The scenario generation process is divided into two stages. Firstly, the dynamic correlation network models temporal correlations based on a dynamic covariance matrix, capturing the time-varying features of renewable energy while enhancing the interpretability of the black-box model. Secondly, the implicit quantile network models the marginal quantile function in a nonparametric, continuous manner, enabling scenario generation through marginal inverse sampling. Experimental results demonstrate that the proposed dynamic correlation quantile network outperforms state-of-the-art methods in quantifying uncertainty and capturing dynamic correlation for short-term renewable energy scenario generation.
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