On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets.
I2MTC(2021)
摘要
Non-intrusive load monitoring (NILM) aims to infer information about the electric consumption of individual loads using the premises' 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.
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关键词
NILM,Load identification,Energy disaggregation,multi-label classification,machine learning
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