On the Impact of the Sequence Length on Sequence-to-Sequence and Sequence-to-Point Learning for NILM
On the Impact of the Sequence Length on Sequence-to-Sequence and Sequence-to-Point Learning for NILM
复制标题
序列长度对 NILM 序列到序列和序列到点学习的影响
DOI：
10.1145/3427771.3427857
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Mazen Bouchur
中科院分区：
文献类型：
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作者：
Andreas Reinhardt;Mazen Bouchur
序列到序列（S2S）和序列到点（S2P）优化方法在负荷分解任务中获得了显著的精度结果。在内部，它们依赖于经过训练的神经网络，从一系列汇总的电力数据中识别出正在考虑的单个设备的功耗。然而，它们最重要的配置参数——要考虑的输入数据样本的数量——大多被设置为一个固定值。因此，在算法输入处可用的历史数据量由所使用的输入数据的采样间隔控制。例如，UK-DALE[5]每6秒提供一次样本，因此599个样本的序列长度（如[9]中提出的）使大约1小时的历史数据可用于分解算法。迄今为止，文献中还没有记录序列长度对NILM性能影响的分析。因此，我们提出了S2S和S2P对其输入序列长度参数变化的敏感性的方法学评估。我们的结果表明，设置每个设备参数值可以改善分解结果；然而，所需的值需要根据经验确定，因为它们与设备的操作持续时间无关。即使只能设置一个值，明智的选择（而不是使用默认值）也可以极大地提高NILM性能。
The Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) optimization methods achieve remarkable accuracy results for load disaggregation tasks. Internally, they rely on neural networks, trained to identify the power consumption of a single appliance under consideration from a sequence of aggregate power data. Their most important configuration parameter - the number of input data samples to consider - is, however, mostly set to a fixed value. As a result thereof, the amount of historical data available at the algorithm's input is governed by the sampling interval of the used input data. For example, UK-DALE [5] provides samples every 6 s, so a sequence length of 599 samples (as proposed in [9]) makes approximately 1 h of historical data available to the disaggregation algorithm. No analyses of the impact of the sequence length on the NILM performance have been documented in literature to date. We hence present a methodological assessment of the sensitivity of S2S and S2P to variations of their input sequence length parameter. Our results show that setting a per-device parameter value leads to improved disaggregation results; however, the required values need to be determined empirically, as they are unrelated to the appliances' operational durations. Even if only a single value may be set, an informed choice (rather than using the default value) can drastically improve NILM performance.
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：
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作者：
Zhang Chaoyun
通讯作者：
Zhang Chaoyun