  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.
