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

• URL: http://arxiv.org/abs/2501.14233v1
• Date: Fri, 24 Jan 2025 04:40:57 GMT
• Title: A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation
• Authors: Xiaochong Dong, Yilin Liu, Xuemin Zhang, Shengwei Mei,
• Abstract summary: 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. The proposed dynamic correlation quantile network outperforms state-of-the-art methods in quantifying uncertainty.
• Score: 5.509260267801284
• License:
• 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.

Related papers

• Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions [16.705621552594643]
  Existing models often struggle with capturing the complex relationships between target variables and covariates. We propose a novel model architecture that leverages the iTransformer for feature extraction from target variables. A cross-attention mechanism is integrated to fuse the outputs of both models, followed by a Kolmogorov-Arnold network mapping. Results demonstrate that the proposed model effectively capture seasonal variations in PV power generation and improve forecasting accuracy.
  arXiv  Detail & Related papers  (2024-12-03T09:16:13Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step. Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces novel deep dynamical models designed to represent continuous-time sequences. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experimental results on oscillating systems, videos and real-world state sequences (MuJoCo) demonstrate that our model with the learnable energy-based prior outperforms existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• On the Trajectory Regularity of ODE-based Diffusion Sampling [79.17334230868693]
  Diffusion-based generative models use differential equations to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models.
  arXiv  Detail & Related papers  (2024-05-18T15:59:41Z)
• Learning Space-Time Continuous Neural PDEs from Partially Observed States [13.01244901400942]
  We introduce a grid-independent model learning partial differential equations (PDEs) from noisy and partial observations on irregular grids. We propose a space-time continuous latent neural PDE model with an efficient probabilistic framework and a novel design encoder for improved data efficiency and grid independence.
  arXiv  Detail & Related papers  (2023-07-09T06:53:59Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Distributed Bayesian Learning of Dynamic States [65.7870637855531]
  The proposed algorithm is a distributed Bayesian filtering task for finite-state hidden Markov models. It can be used for sequential state estimation, as well as for modeling opinion formation over social networks under dynamic environments.
  arXiv  Detail & Related papers  (2022-12-05T19:40:17Z)
• Short-Term Power Prediction for Renewable Energy Using Hybrid Graph Convolutional Network and Long Short-Term Memory Approach [2.218886082289257]
  Short-term power of renewable energy has always been considered a complex regression problem. This paper proposes a new graph neural network-based short-term power forecasting approach.
  arXiv  Detail & Related papers  (2021-11-15T18:15:31Z)
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization [60.73540999409032]
  We show that expressive autoregressive dynamics models generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We also show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer.
  arXiv  Detail & Related papers  (2021-04-28T16:48:44Z)
• Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series [43.86737761236125]
  We propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures. A structured inference network is also designed for enabling inductive analysis.
  arXiv  Detail & Related papers  (2021-03-03T04:10:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.