A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation

TL;DR

This paper introduces a novel data-driven framework that models the dynamic temporal correlations in renewable energy data, improving short-term scenario generation by capturing nonlinear, time-varying atmospheric influences.

Contribution

It presents a decoupled mapping approach combining dynamic correlation modeling with nonparametric marginal quantile functions for renewable energy scenarios.

Findings

Outperforms existing methods in uncertainty quantification.

Effectively captures time-varying correlations in renewable energy data.

Enhances interpretability of the correlation modeling process.

Abstract

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.