基于V-I轨迹的设备识别相似性保持散列

Xingqi Liu, Xuan Liu, Angang Zheng, Hao Chen, Jian Dou
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摘要

非侵入式负载监测(NILM)是一种用于监测建筑物能耗的技术,无需在单个设备上安装硬件。这种方法提供了一种具有成本效益和可扩展的解决方案,以提高能源效率并减少能源使用。NILM的最新进展主要采用深度学习算法进行设备识别。然而,深度学习模型中的大量参数在快速有效地识别设备方面提出了挑战。分析电器的电压-电流(V-I)轨迹特征是一种有效的电器识别技术。本研究提出一种新的哈希方法,学习紧凑的二进制码,以实现高效的电器V-I轨迹识别。具体来说,本文采用一种深度结构,通过获取多层次非线性变换来获取V-I弹道图像特征。随后,我们将这些中间特征与来自最上层的高级视觉数据合并,进行V-I轨迹图像检索过程。这些压缩代码服从三个不同的标准:最小的量化损失,均匀分布的二进制组件,以及不相互依赖的自主比特。因此,网络通过网络传播新获取的查询V-I图像并将网络输出量化为二进制代码表示,从而轻松地对其进行编码,以用于设备识别。通过在PLAID数据集上进行的大量实验,我们证明了与最先进的方法相比,我们的方法具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Similarity preserving hashing for appliance identification based on V-I trajectory
Non-intrusive load monitoring (NILM) is a technique used to monitor energy consumption in buildings without requiring hardware installation on individual appliances. This approach offers a cost-effective and scalable solution to enhance energy efficiency and reduce energy usage. Recent advancements in NILM primarily employ deep-learning algorithms for appliance identification. However, the substantial number of parameters in deep learning models presents challenges in quickly and effectively identifying appliances. An effective technique for appliance identification is analyzing the appliances’ voltage-current (V-I) trajectory signature. This research introduces a novel hashing method that learns compact binary codes to achieve highly efficient appliance V-I trajectory identification. Specifically, this paper uses a profound structure to acquire V-I trajectory image features by acquiring multi-level non-linear transformations. Subsequently, we merge these intermediary traits with high-level visual data from the uppermost layer to carry out the V-I trajectory image retrieval process. These condensed codes are subjected to three distinct standards: minimal loss in quantization, uniformly distributed binary components, and autonomous bits that are not interdependent. As a result, the network easily encodes newly acquired query V-I images for appliance identification by propagating them through the network and quantizing the network’s outputs into binary code representations. Through extensive experiments conducted on the PLAID dataset, we demonstrate the promising performance of our approach compared to state-of-the-art methods.
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