双路径信息融合和双重注意力驱动的全球建模用于太阳辐射预测
Yushi Yang1, Zhanjun Tang1, Zhiyuan Li1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究引入了一种先进的太阳辐射预测模型,通过考虑时间依赖性和因素重要性来提高准确性. 新的双路融合和双重关注方法提高了可再生能源应用的多步预测.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 大气科学与气象学
背景情况:
- 传统的太阳辐射预测方法往往无法捕捉时间动态和影响因素的不同意义.
- 准确的太阳能预测对于高效的可再生能源整合和稳定的电网管理至关重要.
研究的目的:
- 开发和评估一种新的双路信息融合和双重注意力驱动模型,用于增强的多步太阳辐射预测.
- 通过结合自适应特征学习和强大的多尺度数据集成来解决现有方法的局限性.
主要方法:
- 拟议的模型整合了残留注意力时间卷积块 (RACB) 用于适应性特征学习,双路信息融合模块 (DIFM) 用于多尺度特征集成和稳定性,以及双重自我注意力模块 (TSAM) 用于捕捉长期时间依赖.
- 该框架旨在提高多阶段太阳辐射预测的准确性和可靠性.
主要成果:
- 通过对四个数据集进行实验性比较,在六个基准模型中显示出显著的改进.
- 拟议的模型实现了RMSE,MAE和MAPE的平均减少,分别为0.463-2.390 W/m2,0.439-2.005 W/m2和1.3-9.2%,用于单步预测.
- 在数据集中观察到R2值的平均增加从0.008到0.059不等.
结论:
- 开发的双路信息融合和双重注意力模型为太阳辐射预测提供了更高的准确性和稳定性.
- 这种先进的预测技术为优化可再生能源利用和电网运营提供了可靠的替代方案.
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