DSUALMH-一个新的高分辨率NILM数据集

Q4 Energy
C. Rodriguez-Navarro, A. Alcayde, V. Isanbaev, L. Castro-Santos, A. Filgueira-Vizoso, F. G. Montoya
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引用次数: 0

摘要

能源消耗的优化需要一个合理准确的测量,因此一个适当的和先进的监测系统在电力装置的相关电气变量是至关重要的。在这种情况下,可互操作和高度可配置的设备起着至关重要的作用。一个明显的例子是OpenZMeter (OZM),它是一种开源,开放硬件,多用途精密智能电表,可以以高采样率测量各种电气变量,并提供有关电能质量的处理数据。这项工作的目的是展示OZM装置提供的新的高采样频率数据的使用和可能的应用,这些数据比其他低成本电表获得的数据更丰富和更准确。为此,我们使用并调整了开源工具NILMTK。同样,考虑了组合优化(CO)和析因隐马尔可夫模型(FHMM)等两种最著名和最广泛使用的算法的使用,分析了实验研究中获得的结果,并使用不同情况的度量对两种不同的分解算法的性能进行了详细的比较,以及瞬态的合并,并与其他公共数据集进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DSUALMH- A new high-resolution dataset for NILM
The optimisation of energy consumption requires a reasonably accurate measurement, so an appropriate and advanced monitoring system of the relevant electrical variables in the electrical installations is of paramount importance. In this context, interoperable and highly configurable devices play a crucial role. A clear example is the OpenZMeter (OZM) which is an open source, open hardware, multi-purpose precision smart meter that can measure a wide range of electrical variables at a high sampling rate and provide processed data on power quality. The aim of this work is to show the use and possible applications of the new high sampling frequency data provided by the OZM device, which are much richer and more accurate than those obtained with other low-cost electrical meters. For this purpose, the opensource tool NILMTK has been used and adapted. Likewise, the use of two of the best known and most widely used algorithms such as Combinatorial Optimisation (CO) and the Factorial Hidden Markov Model (FHMM) has been considered, analysing the results obtained in the experimental study and offering a detailed comparison of the performance of the two different disaggregation algorithms using metrics for the different cases, as well as the incorporation of transients, and the comparison with other public Datasets.
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来源期刊
Renewable Energy and Power Quality Journal
Renewable Energy and Power Quality Journal Energy-Energy Engineering and Power Technology
CiteScore
0.70
自引率
0.00%
发文量
147
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