Data augmentation for dealing with low sampling rates in NILM
This work addresses data quality issues in NILM for energy disaggregation, but it is incremental as it applies existing augmentation techniques to a specific domain.
The paper tackles the problem of low sampling rates in Non-Intrusive Load Monitoring (NILM) data, which hinders device recognition and power estimation, by experimenting with data augmentation methods to increase sampling rates, resulting in higher F-score measurements for appliance consumption estimation.
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.