Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China.
Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China.
Sensors (Basel). 2021 Aug 9;21(16):5366. doi: 10.3390/s21165366.
To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.
为了实现碳中和目标，住宅部门的节能需求大幅增加。非侵入式负荷监测（NILM）作为一种很有前途的监测和管理住宅负荷的范例，现有的研究要么缺乏对实际案例的可扩展性，要么过于关注识别精度。本文提出了一种高精度、超稀疏样本和实时计算的住宅电器 NILM 方法。该方法包括三个步骤：事件检测、特征提取和负荷识别。首先提出了一种基于小波分解的标准差倍数（WDSDM）方法，以增强具有复杂启动过程的电器的事件检测能力。结果表明，假检测率仅为十六分之一，消耗时间仅为 0.77 秒。此外，引入了 NILM 的一个基本特征，即过冲倍数（它有助于将类似电器的平均识别精度从 82.1%提高到 100%）。此外，在采样频率为 6.25 kHz 的情况下，仅使用一个训练样本，经过修正的加权 K-最近邻（KNN）和过冲倍数的组合就能实现 100%的电器识别精度。该方法为可预见的未来高效、用户友好、可扩展和现实可行的能源管理系统提供了思路。
