论文标题
基于时间和峰值需求模式的两阶段建筑能源消耗聚类
Two-stage building energy consumption clustering based on temporal and peak demand patterns
论文作者
论文摘要
分析智能电表数据以了解能源消耗模式有助于公用事业和能源提供商执行自定义的需求响应操作。现有的能源消耗细分技术使用的假设可能导致群集在代表其成员时的质量降低。我们通过引入两阶段聚类方法来解决此限制，该方法更准确地捕获了载荷形状的时间模式和峰值需求。在第一阶段，通过允许大量簇来准确捕获能量使用模式的变化，并通过考虑形状未对准来提取簇质心，从而聚集了负载形状。在第二阶段，通过使用动态时间翘曲，合并了相似的质心和功率幅度范围的簇。与基线方法相比，我们使用了由约250个家庭（约15000个配置文件）组成的三个数据集（约15000个配置文件）来证明性能的改进，并讨论了对能源管理的影响。
Analyzing smart meter data to understand energy consumption patterns helps utilities and energy providers perform customized demand response operations. Existing energy consumption segmentation techniques use assumptions that could result in reduced quality of clusters in representing their members. We address this limitation by introducing a two-stage clustering method that more accurately captures load shape temporal patterns and peak demands. In the first stage, load shapes are clustered by allowing a large number of clusters to accurately capture variations in energy use patterns and cluster centroids are extracted by accounting for shape misalignments. In the second stage, clusters of similar centroid and power magnitude range are merged by using Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the performance improvement, compared to baseline methods, and discuss the impact on energy management.
