论文标题
高斯流程中的挑战，用于非侵入性负载监测
Challenges in Gaussian Processes for Non Intrusive Load Monitoring
论文作者
论文摘要
非侵入性负载监测（NILM）或能量分解旨在将家庭总能源消耗分解为组成型设备。先前的工作表明，提供能量破裂可以帮助人们节省多达15％的能源。近年来，深度神经网络（Deep NNS）在NILM领域取得了显着进步。在本文中，我们证明了尼尔姆的高斯过程（GPS）的性能。由于三个主要原因，我们选择GPS：i）GPS固有地对不确定性建模； ii）无限NNS和GP之间的等效性； iii）通过适当设计内核，我们可以合并域专业知识。我们探索并提出了将GP方法应用于NILM的挑战。
Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save up to 15\% of energy. In recent years, deep neural networks (deep NNs) have made remarkable progress in the domain of NILM. In this paper, we demonstrate the performance of Gaussian Processes (GPs) for NILM. We choose GPs due to three main reasons: i) GPs inherently model uncertainty; ii) equivalence between infinite NNs and GPs; iii) by appropriately designing the kernel we can incorporate domain expertise. We explore and present the challenges of applying our GP approaches to NILM.
