多站点电力负荷监测的光滑非负张量分解
Smooth nonnegative tensor factorization for multi-sites electrical load monitoring
摘要：
从智能电表收集的负载曲线分析是许多能源管理任务的关键步骤，从消费预测到客户表征和负载监控。在这方面的贡献，我们提出了一个模型的基础上的非负张量因式分解的功能制定，并获得相应的优化问题的更新。我们在多站点负荷曲线分解的具体示例中展示了该公式如何有助于1）展示平滑的日内消耗模式和2）考虑外部变量，如外部温度。通过展示基于所获得的分解的所观察到的站点的有意义的聚类，在模拟和真实数据上证明了这些益处。
英文摘要：
The analysis of load curves collected from smart meters is a key step for many energy management tasks ranging from consumption forecasting to customers characterization and load monitoring. In this contribution, we propose a model based on a functional formulation of nonnegative tensor factorization and derive updates for the corresponding optimization problem. We show on the concrete example of multi-sites load curves disaggregation how this formulation is helpful for 1) exhibiting smooth intraday consumption patterns and 2) taking into account external variables such as the outside temperature. The benefits are demonstrated on simulated and real data by exhibiting a meaningful clustering of the observed sites based on the obtained decomposition.
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