住宅用电需求概况的集群和解释性
Sarra Kallel1, Manar Amayri1, Nizar Bouguila1
1Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究比较了电力消耗模式的集群算法,发现软集群在分离组中表现出色. 它还引入了可解释的能源使用规则的决策树.
科学领域:
- 数据科学数据科学数据科学
- 能源系统分析 能源系统分析
- 机器学习 机器学习
背景情况:
- 高效的能源管理需要从电力负载数据中识别消费模式.
- 智能电表和物联网设备提供细粒度的实时电力使用数据.
- 聚类是分析消费行为的关键,但解释性往往缺乏.
研究的目的:
- 为了比较硬和软集群算法来分割电力消耗配置文件.
- 用各种集群验证指数 (CVI) 评估集群性能.
- 通过决策树模型提高聚类结果的解释性.
主要方法:
- 将K-Means,K-Medoids (硬集群) 与模糊的C-Means,高斯混合模型 (软集群) 进行比较.
- 使用五个聚类验证指数 (CVI) 评估的绩效,用于凝聚力和分离.
- 开发了轴对齐和稀疏的斜视决策树,用于生成人类可读的集群分配规则.
主要成果:
- 与硬集群相比,软集群方法表明集群间的分离优越.
- 集群内部特征在所有测试的集群算法中都表现出类似的行为.
- 确定了可靠和敏感的CVI,以评估独立于算法选择的集群性能.
结论:
- 软集群可以更好地对电力消耗配置文件进行细分.
- 决策树提供了一个平衡解释性和复杂性的框架,以集群解释.
- 调查结果支持公用事业提供商,政策制定者和研究人员优化能源需求分析.
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