%0 Conference Paper %1 noauthororeditor2018augmentation %A Quy, Tay Le %A Zerr, Sergej %A Ntoutsi, Eirini %A Nejdl, Wolfgang %B 4th International Workshop on Non-Intrusive Load Monitoring, University of Texas, Austin, TX, USA %D 2018 %K myown %T Data augmentation for dealing with low sampling rates in NILM %X Data plays a crucial role in evaluation 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 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.

@inproceedings{noauthororeditor2018augmentation, abstract = {Data plays a crucial role in evaluation 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 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.}, added-at = {2020-03-18T10:37:06.000+0100}, author = {Quy, Tay Le and Zerr, Sergej and Ntoutsi, Eirini and Nejdl, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/2ce476f5fcef6f9130b8e34f4f004f081/zerr}, booktitle = {4th International Workshop on Non-Intrusive Load Monitoring, University of Texas, Austin, TX, USA}, interhash = {9afed31f5dffeff96a96eb2fe26862db}, intrahash = {ce476f5fcef6f9130b8e34f4f004f081}, keywords = {myown}, timestamp = {2020-03-18T10:40:19.000+0100}, title = {Data augmentation for dealing with low sampling rates in NILM}, year = 2018 }