一种轻量级的方法,用于集成本地负载预测和控制活跃配电网络中的边缘计算
Yubo Wang1,2, Xingang Zhao1, Kangsheng Wang3
1North China Electric Power University, Beijing 102206, China.
iScience
|August 12, 2024
概括
本研究介绍了一种轻量级的自适应集体学习方法,用于准确的本地负载预测和活跃分布网络中的预测控制,优化用于资源受限的边缘计算设备.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 边缘计算设备面临资源限制,在活跃的分发网络中挑战准确的负载预测.
- 动态负载特征需要先进的预测方法来实现高效的网络运行.
研究的目的:
- 开发一种轻量级的,适应性的集体学习方法,用于在资源有限的边缘计算场景中进行局部负载预测和预测控制.
- 为了减少模型复杂性和计算开销,而不会影响预测准确度.
主要方法:
- 适应性稀疏集成以最大限度地减少模型规模.
- 自动编码器用于缩小模型变量,减少计算和存储.
- 适应性校正方法用于持续的模型适应性.
- 多次时间尺度预测控制,用于综合预测和控制.
主要成果:
- 提出的方法成功地将边缘设备上的模型复杂性降到最低.
- 预测准确度保持在与基准方法相提并论的水平.
- 这种方法使得有效的局部负载预测和预测性控制成为可能.
结论:
- 轻量级的自适应集体学习方法对于边缘设备上的活跃分发网络是有效的.
- 这种方法解决了资源限制,同时确保了高预测性能.
- 该研究促进了边缘计算环境中的协作本地负载预测和控制.
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