论文标题
聚集的分层聚类与家庭负载曲线聚类的动态时间扭曲
Agglomerative Hierarchical Clustering with Dynamic Time Warping for Household Load Curve Clustering
论文作者
论文摘要
能源公司经常实施各种需求响应（DR）计划，以更好地匹配电力需求和供应，从而为消费者的激励措施减少关键时期的需求。根据客户的消费模式对客户进行分类，可以针对DR的特定消费者组。传统的聚类算法使用标准距离测量来找到两个点之间的距离。聚类算法（例如K-均值，K-模拟物和高斯混合物模型）产生的结果取决于聚类参数或初始簇。相比之下，我们的方法论使用一种基于形状的方法，该方法将聚集的分层聚类（AHC）与动态时间扭曲（DTW）结合在一起，根据其消耗模式对住宅家庭的日常负载曲线进行分类。尽管DTW寻求两条负载曲线之间的最佳对齐，但AHC提供了现实的初始簇中心。在本文中，我们使用不同的距离测量方法将结果与其他聚类算法（例如K-均值，K-Medoids和GMM）进行了比较，并且我们表明，使用DTW的AHC优于其他聚类算法，并且需要更少的簇。
Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify residential households' daily load curves based on their consumption patterns. While DTW seeks the optimal alignment between two load curves, AHC provides a realistic initial clusters center. In this paper, we compare the results with other clustering algorithms such as K-means, K-medoids, and GMM using different distance measures, and we show that AHC using DTW outperformed other clustering algorithms and needed fewer clusters.
