Data Requirements for Applying Machine Learning to Energy Disaggregation
Data Requirements for Applying Machine Learning to Energy Disaggregation
复制标题
将机器学习应用于能源分解的数据要求
DOI：
10.3390/en12091696
复制
发表时间：
2019
期刊：
影响因子：
3.2
通讯作者：
Wonjong Rhee
中科院分区：
文献类型：
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作者：
Changho Shin;Seungeun Rho;Hyoseop Lee;Wonjong Rhee
能量分解，或非侵入性负荷监测(NILM)，是一种分离家庭总用电量信息的技术。虽然这项技术是在1992年发展起来的，但它的实际应用和大规模部署一直相当有限，可能是因为常用的数据集不足以进行NILM研究。在这项研究中，我们报告了一个新收集的数据集的发现，该数据集包含58个房屋的10赫兹采样数据。该数据集不仅包含聚合测量值，还包含三种类型的电器的单个电器测量值。通过将三种分类算法(Vanilla DNN(深度神经网络)、ML(机器学习)和CNN(卷积神经网络)结合超参数调整)和最新的回归算法(子任务门控网络)应用于新的数据集，我们发现当数据采样率太低或数据集中不同的房屋数太小时，NILM的性能会受到显著限制。在研究界流行的著名的NILM数据集不符合这些要求。我们的结果表明，应该使用更高质量的数据集来加快NILM研究的进程。
Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.
使用神经网络进行序列到点学习，用于非侵入式负载监控
DOI：
10.48550/arxiv.1612.09106
发表时间：
2016
期刊：
arXiv e-prints
影响因子：
--
作者：
Zhang Chaoyun
通讯作者：
Zhang Chaoyun