相关实验视频
通过由忍者算法优化的时空图卷积网络增强绿色预测.
Mona Ahmed Yassen1,2, Amel Ali Alhussan3, Mohamed Gamal Abdel-Fattah4
1Faculty of Artificial Intelligence, Hours University, New Damietta, Egypt. Monagaffer@std.mans.edu.eg.
Scientific reports
|November 7, 2025
概括
预测绿色的生产现在更准确地使用空间时间图卷积网络 (STGCN) 与忍者优化算法 (NiOA) 相结合. 这种先进的系统可以提高可持续能源解决方案的预测准确性和效率.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 计算机科学 计算机科学
背景情况:
- 可持续的基础设施发展对于应对气候变化至关重要.
- 通过可再生电解生产的绿色是这一转变的关键组成部分.
- 由于可变的环境和系统因素,准确预测绿色的生产具有挑战性.
研究的目的:
- 开发一个改进的系统来预测绿色的生产.
- 解决影响生产的时间和空间因素的变化.
- 提高可再生能源建模的准确性和效率.
主要方法:
- 使用一个时空图形卷积网络 (STGCN).
- 引入了一种新的算法,即忍者优化算法 (NiOA),用于优化.
- 采用二进制 NiOA 进行特征选择和连续 NiOA用于模型架构和变量优化.
主要成果:
- 该STGCN模型实现了0.8769的R2和0.00375.5的MSE.
- 与NiOA集成的STGCN表现出显著改善的结果,R2为0.9815.
- STGCN-NiOA模型实现了显著较低的MSE,表明预测准确度提高.
结论:
- STGCN与NiOA的整合为准确的绿色生产预测提供了一个强大的框架.
- 像NiOA这样的自适应式元启发学显示出了改善可再生能源系统建模的重大前景.
- 该战略为建模可再生能源系统和加强绿色倡议提供了可靠的方法.
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