标题： 使用高维回归混合模型对电力消费者进行聚类 显示英文标题

标题： Clustering electricity consumers using high-dimensional regression mixture models

摘要： 大规模的个体（家庭、中小企业）消费信息现在通过新的计量技术和智能电网得到提供。这些数据的主要应用是不同尺度上的负荷剖面和预测。基于负荷分类的客户细分是实现这些目标的一种自然方法。我们提出了一种基于高维回归模型混合的新方法。我们方法的新颖之处在于我们专注于揭示对应于不同回归模型的类别或聚类。因此，这些类别可以用于每个类别的剖面描绘以及预测，或者以统一视角进行自下而上的预测。我们考虑了一个爱尔兰个人消费者的实际数据集，包含4,225个计量表，每个表每天有48个半小时的计量读数，时间为从2010年1月1日到2010年12月31日，以此来证明我们方法的可行性。 显示英文摘要

摘要： Massive informations about individual (household, small and medium enterprise) consumption are now provided with new metering technologies and the smart grid. Two major exploitations of these data are load profiling and forecasting at different scales on the grid. Customer segmentation based on load classification is a natural approach for these purposes. We propose here a new methodology based on mixture of high-dimensional regression models. The novelty of our approach is that we focus on uncovering classes or clusters corresponding to different regression models. As a consequence, these classes could then be exploited for profiling as well as forecasting in each class or for bottom-up forecasts in a unified view. We consider a real dataset of Irish individual consumers of 4,225 meters, each with 48 half-hourly meter reads per day over 1 year: from 1st January 2010 up to 31st December 2010, to demonstrate the feasibility of our approach.