在智能能源消耗细分的边缘上无监督的混合模型,具有特征突出性
Hussein Al-Bazzaz1, Muhammad Azam1, Manar Amayri1
1Concordia's Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1M8, Canada.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一个用于分析高分辨率智能电表数据的新框架,改进了能源消耗模式的识别. 新模型提高了公用事业公司的集群精度.
科学领域:
- 数据挖掘和机器学习
- 能源系统分析 能源系统分析
背景情况:
- 智能电表数据的细节性显著增加,对传统的集群方法提出了挑战.
- 高分辨率数据显示非高斯分布,未知集群数量和高维度,使模式分析复杂化.
研究的目的:
- 为高分辨率智能电表数据的有效集群开发一个创新的学习框架.
- 为了实现并发的功能和模型选择,以改善能源消耗模式的辨别.
主要方法:
- 期望最大化算法的整合与最小消息长度标准.
- 关于具有特征突出性的局限不对称通用高斯混合模型的建议.
- 在合成和现实世界的智能电表数据集中使用三个特征提取方法进行验证.
主要成果:
- 与最先进的方法相比,拟议的算法显示出优越的集群有效性.
- 识别的集群有效地突出了住宅能源消费模式的变化.
- 与混合模型的无界变体相比,实现了7.828%的平均性能改善.
结论:
- 开发的框架为公用事业公司在减少需求的努力中提供了可操作的见解.
- 该方法是强大的,适用于现实世界的智能电表环境,包括边缘云计算.
- 拟议的边界不对称的通用高斯混合模型比其他测试模型具有显著的优势.
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