Computational study demonstrates improved energy disaggregation across benchmark datasets, indicating the efficacy of bidirectional transformers for smart grid monitoring.
Non-intrusive load monitoring (NILM) based energy disaggregation is the decomposition of a system's energy into the consumption of its individual appliances. Previous work on deep learning NILM algorithms has shown great potential in the field of energy management and smart grids. In this paper, we propose BERT4NILM, an architecture based on bidirectional encoder representations from transformers (BERT) and an improved objective function designed specifically for NILM learning. We adapt the bidirectional transformer architecture to the field of energy disaggregation and follow the pattern of sequence-to-sequence learning. With the improved loss function and masked training, BERT4NILM outperforms state-of-the-art models across various metrics on the two publicly available datasets UK-DALE and REDD.
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Yue et al. (2020) studied this question.
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