@conference {Ahajjam2021,
	title = {On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets},
	booktitle = {Conference Record - IEEE Instrumentation and Measurement Technology Conference},
	volume = {2021-May},
	year = {2021},
	note = {cited By 0},
	abstract = {Non-intrusive load monitoring (NILM) aims to infer information about the electric consumption of individual loads using the premises{\textquoteright} aggregate consumption. In this work, we target supervised multi-label classification for non-intrusive load identification. We describe how we have created a new dataset from Moroccan households using a low sampling frequency. Then, we analyze the performance of three machine learning models for NILM, and investigate the impact of signal input length on performance. {\textcopyright} 2021 IEEE.},
	keywords = {Aggregate consumption, Classification (of information), Electric consumption, Load identification, Machine learning models, Measurement, Multi label classification, Non-intrusive, Nonintrusive load monitoring, Sampling frequencies},
	doi = {10.1109/I2MTC50364.2021.9460059},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113709883\&doi=10.1109\%2fI2MTC50364.2021.9460059\&partnerID=40\&md5=2f6ca1a0b8790dce2a085d0e0ff10cf4},
	author = {Ahajjam, M.A. and Essayeh, C. and Ghogho, M. and Kobbane, A.}
}
