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

2025-01-24

AI Summary

To address the modeling challenge of uncertainty arising from the strong nonlinearity, time-varying dynamics, and weather dependence of renewable energy generation, this paper proposes a decoupled dynamic temporal scenario generation framework. Methodologically, the first stage introduces a dynamic covariance network to explicitly capture time-varying temporal dependencies while enhancing the interpretability of black-box models; the second stage employs an implicit quantile network (IQN) for nonparametric marginal distribution fitting, followed by marginal inverse sampling and decoupled joint distribution regression to generate high-fidelity scenarios. Theoretically, Proper Scoring Rules are incorporated to ensure model calibration and statistical validity. Evaluated on short-term wind and solar power forecasting, the framework achieves state-of-the-art performance in both uncertainty quantification accuracy and dynamic correlation capture, significantly outperforming existing baselines.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Renewable Energy Forecasting
• Weather Variability
• Energy Trend Analysis

Innovation

• Dynamic Time Correlation
• Decoupling Mapping
• Renewable Energy Forecasting