论文标题
通过卷积稀疏编码无监督的能量分解
Unsupervised energy disaggregation via convolutional sparse coding
论文作者
论文摘要
在这项工作中，提出了一种配备智能电表的私人家庭中无监督能量分解的方法。该方法旨在将功耗分类为主动或被动的，从而赋予居民活动和存在而无需直接互动的能力。这为诸如对私人房屋的非侵入性健康监测之类的应用奠定了基础。 所提出的方法是基于最小化合适的能量功能的方法，该功能为此iPALM（惯性近端交替线性化最小化）算法被采用，表明满足了确保收敛性的各种条件。 为了确认所提出的方法的可行性，提供了半合成测试数据集的实验，并提供了与现有监督方法的比较。
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active or passive, granting the ability to report on the residents' activity and presence without direct interaction. This lays the foundation for applications like non-intrusive health monitoring of private homes. The proposed method is based on minimizing a suitable energy functional, for which the iPALM (inertial proximal alternating linearized minimization) algorithm is employed, demonstrating that various conditions guaranteeing convergence are satisfied. In order to confirm feasibility of the proposed method, experiments on semi-synthetic test data sets and a comparison to existing, supervised methods are provided.
