对称的时空学习网络与稀疏计表图用于制造系统中短期能源消耗预测
Jianhua Guo1, Mingdong Han2,1, Chunlin Xu1
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China.
Heliyon
|August 7, 2024
概括
本研究引入了一种新型的对称时空学习网络与稀量计图 (SSTLN-SMG),用于制造业中准确的多节点能耗预测. 与现有方法相比,该方法显著减少了预测错误.
科学领域:
- 制造系统工程 制造系统工程
- 人工智能的人工智能
- 能源管理 能源管理
背景情况:
- 短期能源预测对于制造业务至关重要,例如异常检测和节能.
- 现有的单节点预测方法在参数收集和建模方面面临挑战.
- 目前的多节点预测方法缺乏足够的知识指导,并与复杂的时空关系作斗争.
研究的目的:
- 开发一种先进的方法,用于制造系统中准确的多节点能源消耗预测.
- 通过结合流程知识和解决时空复杂性的方法来提高预测性能.
- 为实时能源管理和控制提供一个强大的框架.
主要方法:
- 引入 Sparse Meter Graph (SMG) 来表示过程知识,包括生产节点和材料/能源流.
- 使用图形卷积网络 (GCN) 和封闭线性单位 (GLU) 开发一个对称时空学习网络 (SSTLN).
- SSTLN旨在平衡空间和时间特征提取,细节捕获和噪音抑制.
主要成果:
- 与最先进的方法相比,拟议的SSTLN-SMG方法在多节节能消耗中实现了较低的预测错误.
- 实验验证是使用来自型材工厂的数据集进行的.
- 该方法在使用变形计数图表或学习网络的方法中表现出更高的性能.
结论:
- SSTLN-SMG在多节点能源消耗预测准确度方面取得了重大进展.
- 通过SMG集成的过程知识有效地引导SSTLN捕捉复杂的时空动态.
- 这种方法为提高制造环境中的能源效率和操作控制提供了一个有希望的解决方案.
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