基于启发式的联合学习与适应性超参数调,用于家庭能源预测
Liana Toderean1, Mihai Daian1, Tudor Cioara2
1Distributed Systems Research Laboratory, Computer Science Department, Technical University of Cluj-Napoca, G. Barițiu 26-28, Cluj-Napoca, 400027, Romania.
Scientific reports
|April 12, 2025
概括
这项研究引入了一种层次的联合学习方法,用于预测电荷,提高AI模型对各种家庭能源数据的准确性. 它优化了模型聚合和超参数调整,以减少通信开销,实现更好的预测.
科学领域:
- 人工智能的人工智能
- 能源系统 能源系统
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 能够在边缘设备上训练人工智能模型,用于电荷预测.
- 在FL中非IID数据降低了预测准确性,需要自适应式超参数优化.
- 现有的FL方法与不同质的家庭能源消耗模式作斗争.
研究的目的:
- 开发一种新的分层联合学习解决方案,用于高效的电荷预测.
- 通过通过自适应式超参数调整解决非IID数据挑战来提高预测准确性.
- 优化模型聚合并减少边缘设备上的计算负载.
主要方法:
- 在边缘按能源配置文件对家庭进行聚类,并在雾层面进行聚合.
- 层次模拟回火用于优化联合模型聚合,优先考虑高性能模型.
- 基于遗传算法的超参数优化,以减少边缘节点计算负担.
- 评估预测准确性与联邦平均值对比.
主要成果:
- 对家庭能源模式的平均预测准确度有显著改善.
- 证明能够有效地捕捉复杂的能源消耗动态的能力.
- 不同网络层的网络流量影响保持在30KB以下.
- 通过超参数调整,减少了30%的模型更新大小和通信回合.
结论:
- 提出的等级联合学习方法提高了电荷预测的准确性.
- 适应性超参数调整对于提高非IID能源数据的FL性能至关重要.
- 该解决方案提供了效率提升,特别有利于资源有限的边缘环境.
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