A Case Study on Obstacles to Feasible NILM Solutions for Energy Disaggregation in Quebec Residences. Hosseini, S. S., Delcroix, B., Henao, N., Agbossou, K., & Kelouwani, S. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, of BuildSys '22, pages 363–367, New York, NY, USA, 2022. Association for Computing Machinery.
Paper doi abstract bibtex 3 downloads The Non-Intrusive Load Monitoring (NILM) concept is suggested as a practical means for energy monitoring at the most disaggregated level. Notwithstanding, a viable solution to this idea for residential applications should overcome its common and specific issues raised by technical specifications of the case study. Knowing the fact that the former has been dealt with through basic research for many years, this study presents an applied research to examine actual implementations. It focuses on load disaggregation in Quebec residences by proposing a combinatory approach based on supervised and unsupervised machine learning techniques. The proposed method aims to identify major appliances by extracting overall heating demand from the aggregated one first while exploiting low sampling rate data of active power as the only source of information. The results of this work emphasize real circumstances under which existing NILM methods can be challenged. From a realistic viewpoint, this paper discusses essential remarks inevitable to achieve a fruitful NILM system, specifically, for the Quebec case.
@inproceedings{10.1145/3563357.3566151,
author = {Hosseini, Sayed Saeed and Delcroix, Benoit and Henao, Nilson and Agbossou, Kodjo and Kelouwani, Sousso},
title = {A Case Study on Obstacles to Feasible NILM Solutions for Energy Disaggregation in Quebec Residences},
year = {2022},
isbn = {9781450398909},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3563357.3566151},
doi = {10.1145/3563357.3566151},
abstract = {The Non-Intrusive Load Monitoring (NILM) concept is suggested as a practical means for energy monitoring at the most disaggregated level. Notwithstanding, a viable solution to this idea for residential applications should overcome its common and specific issues raised by technical specifications of the case study. Knowing the fact that the former has been dealt with through basic research for many years, this study presents an applied research to examine actual implementations. It focuses on load disaggregation in Quebec residences by proposing a combinatory approach based on supervised and unsupervised machine learning techniques. The proposed method aims to identify major appliances by extracting overall heating demand from the aggregated one first while exploiting low sampling rate data of active power as the only source of information. The results of this work emphasize real circumstances under which existing NILM methods can be challenged. From a realistic viewpoint, this paper discusses essential remarks inevitable to achieve a fruitful NILM system, specifically, for the Quebec case.},
booktitle = {Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
pages = {363–367},
numpages = {5},
keywords = {baseboard heaters, load disaggregation, non-intrusive load monitoring (NILM), datasets},
location = {Boston, Massachusetts},
series = {BuildSys '22}
}
Downloads: 3
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