基于邻近组件分析和规范化的极端机器学习的家庭能源管理系统中的负载识别
Thales W Cabral1, Fernando B Neto2, Eduardo R de Lima3
1Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究通过改进设备识别来增强家庭能源管理系统 (HEMS). 邻近组件分析 (NCA) 和规范极端学习机器 (RELM) 提高了确定家庭负载的准确性和可靠性.
科学领域:
- 能源管理 能源管理
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 家庭能源管理系统 (HEMS) 对于优化住宅能源消耗至关重要.
- 精确的负载识别,识别活跃的设备,增强HEMS的稳定性,但需要进一步的探索.
- 现有的方法在分类性能,类间可分离性和模型可靠性方面面临挑战.
研究的目的:
- 改进家庭能源管理系统 (HEMS) 的负载识别技术.
- 在识别家用电器方面提高分类性能和模型可靠性.
- 探索邻近组件分析 (NCA) 和规范极端学习机器 (RELM) 在先进能源管理方面的潜力.
主要方法:
- 利用邻近组件分析 (NCA) 来提取特征,重点是改善类分离性.
- 雇佣的规范化极端学习机器 (RELM) 用于家用电器的分类和识别.
- 通过使用关键绩效指标对最先进的方法进行评估.
主要成果:
- 在设备识别中实现了97.24%的高精度和97.14%的加权F1-Score.
- 以0.9388的卡帕指数证明了增强的可靠性,超过了竞争对手的分类器.
- 结合NCA和RELM方法显著提高了负载识别性能.
结论:
- NCA和RELM的整合为HEMS的负载识别提供了一种开创性和有效的方法.
- 机器学习技术,特别是NCA和RELM,在住宅环境中推进能源管理方面显示出重大前景.
- 这项研究通过改进设备识别能力,为更强大,更可靠的HEMS做出了贡献.
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