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SolarImputer: Conditional diffusion-model based spatio-temporal solar irradiance imputation model
DOI:10.1016/j.renene.2026.125822.png)
摘要
En 中文
• 一种基于扩散模型的时空辐照度插补新方法。 • 基于物理的预填充为缺失数据提供更好的初始化。 • 基于小波和图的多模态特征增强一致性和准确性。 • 在所有区域和缺失率下均表现出更优的准确性和鲁棒性。
Keyword:
SolarImputer
diffusion model
spatio-temporal irradiance
imputation
wavelet and graph features
期刊
IF:
9.1
论文数:
2.6W
被引数:
12.1W
机构
引用论文
Methodology Based on BERT (Bidirectional Encoder Representations from Transformers) to Improve Solar Irradiance Prediction of Deep Learning Models Trained with Time Series of Spatiotemporal Meteorological Information
Forecasting
IF0
EnergyDiff: Universal Time-Series Energy Data Generation Using Diffusion ModelsEnergyDiff: 基于扩散模型的通用时间序列能源数据生成
Deep learning algorithms for very short term solar irradiance forecasting: A survey用于非常短期太阳辐照度预测的深度学习算法: 一项调查
Evaluation of data imputation models for building-integrated photovoltaic systems with practical performance and reproducibility建筑集成光伏系统数据插补模型的评估:基于实际性能与可重复性
Solar Energy
IF6.6
Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage
SOLAR ENERGY
IF6.6
Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective重新思考扩散模型在缺失数据插补中的应用:一种梯度流视角

