论文标题
尼尔姆作为回归与分类问题：阈值的重要性
NILM as a regression versus classification problem: the importance of thresholding
论文作者
论文摘要
非侵入性负载监控（NILM）旨在仅通过知道总体功率负载来预测家庭家用电器的状态或消费。尼尔姆可以被称为回归问题，也可以通常作为分类问题。智能计收集的大多数数据集都允许自然定义回归问题，但是相应的分类问题是派生的，因为它需要通过阈值方法将功率信号转换为每个设备的状态。我们对处理三种不同的阈值方法来执行此任务，并讨论了它们在英国销售数据集的各种设备上的差异。我们分析了深度学习最先进的体系结构在回归和分类问题上的性能，并引入了选择最方便的阈值方法的标准。
Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load. NILM can be formulated as regression problem or most often as a classification problem. Most datasets gathered by smart meters allow to define naturally a regression problem, but the corresponding classification problem is a derived one, since it requires a conversion from the power signal to the status of each device by a thresholding method. We treat three different thresholding methods to perform this task, discussing their differences on various devices from the UK-DALE dataset. We analyze the performance of deep learning state-of-the-art architectures on both the regression and classification problems, introducing criteria to select the most convenient thresholding method.
