A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation
This work addresses scenario generation for renewable energy forecasting, which is crucial for grid management, but appears incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of generating renewable energy scenarios by modeling nonlinear and time-varying atmospheric influences, proposing a dynamic temporal correlation framework that outperformed state-of-the-art methods in uncertainty quantification and capturing dynamic correlations.
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.