计算机科学
云计算
稳健性(进化)
辐照度
数据挖掘
情态动词
遥感
云量
块(置换群论)
人工智能
实时计算
量子力学
基因
操作系统
物理
生物化学
地质学
化学
高分子化学
数学
几何学
作者
Shuo Shan,Chenxi Li,Yiye Wang,Shixiong Fang,Kanjian Zhang,Shuo Shan
标识
DOI:10.1016/j.eswa.2023.122925
摘要
Short-term spatio-temporal solar irradiance forecasting plays a pivotal role in scheduling and dispatching energy for distributed energy systems. Fluctuations in cloud cover can be monitored via satellite cloud imagery, which directly impacts irradiance. However, integrating and fusing multi-source heterogeneous data, such as satellite cloud images and ground monitoring data from distributed stations, remains challenging. Here, a spatio-temporal irradiance forecast model is proposed based on multi-modal deep learning model to predict global horizontal irradiance 30 min ahead. To address the feature extraction of heterogeneous data, a dual-channel structure consisting of a time-series processing block and a satellite cloud image processing block is developed to enable parallel processing of multi-modal features. In order To tightly couple cloud images and historical time series at the feature level, maximum mean discrepancy of these two feature is used to help the fusion of heterogeneous data. Furthermore, a self-attention mechanism is employed to construct adaptive inter-region information weights to enhance spatio-temporal representation ability. The evaluation of the method is conducted on open-access datasets from six locations in Jiangsu Province, China. Experimental results demonstrate that the proposed model efficiently utilizes heterogeneous data to improve prediction accuracy under various conditions and enhances model robustness, reducing RMSE by 2.8%–20.58%. Meanwhile, the proposed end-to-end model reduces training and deployment costs for real-world use.
科研通智能强力驱动
Strongly Powered by AbleSci AI