处理NILM低采样率的数据扩充
Data augmentation for dealing with low sampling rates in NILM
摘要：
数据在评估NILM算法的性能方面具有重要作用。NILM算法的最佳性能是通过高质量的评估数据实现的。然而，许多现有的真实世界数据集的采样质量较低，并且通常存在差距，缺乏某些记录期间的数据。因此，在这样的数据中，NILM算法几乎不能识别设备并正确地估计它们的功耗。提高这些能量分解方法的性能的一个重要步骤是提高数据集的质量。在本文中，我们进行了实验，使用几种方法来提高低采样率数据的采样率。我们的研究结果表明，低频数据的增强可以支持所考虑的NILM算法在估计器具的消费与更高的F分数测量。
英文摘要：
Data have an important role in evaluating the performance of NILM algorithms. The best performance of NILM algorithms is achieved with high-quality evaluation data. However, many existing real-world data sets come with a low sampling quality, and often with gaps, lacking data for some recording periods. As a result, in such data, NILM algorithms can hardly recognize devices and estimate their power consumption properly. An important step towards improving the performance of these energy disaggregation methods is to improve the quality of the data sets. In this paper, we carry out experiments using several methods to increase the sampling rate of low sampling rate data. Our results show that augmentation of low-frequency data can support the considered NILM algorithms in estimating appliances' consumption with a higher F-score measurement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
