Clustering disaggregated load profiles using a Dirichlet process mixture model
Clustering disaggregated load profiles using a Dirichlet process mixture model
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DOI：
10.1016/j.enconman.2014.12.080
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发表时间：
2015-03
影响因子：
10.4
通讯作者：
R. Granell;C. Axon;D. Wallom
中科院分区：
文献类型：
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作者：
R. Granell;C. Axon;D. Wallom
在住宅和商业部门中，越来越多的大量用电数据的可用性提高了挖掘数据以有利于消费者和网络运营的可能性。我们提出了一个贝叶斯非参数模型聚类负荷配置文件从家庭和企业的处所。评估表明，我们的模型执行以及其他流行的聚类方法，但不像大多数其他方法，它不需要由用户预先确定的集群的数量。我们使用所谓的“中餐厅流程”方法来求解该模型，利用狄利克雷多项分布。聚类的数量随着数据量的增长而增长，使得该技术适合扩展到大型数据集。我们能够证明，该模型可以区分集群成员之间的国籍、家庭规模和居住类型等特征。
The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called ‘Chinese restaurant process’ method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships.