@inproceedings{a9edb6e818c34495985b93208eab5dcb,
title = "BERT4NILM: A Bidirectional Transformer Model for Non-Intrusive Load Monitoring",
abstract = "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 NILMalgorithms 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 adaptthe 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.",
keywords = "Deep Learning, Energy Disaggregation, NILM, Neural Network, Non-Intrusive Load Monitoring, Transformer",
author = "Zhenrui Yue and Witzig, {Camilo Requena} and Daniel Jorde and Jacobsen, {Hans Arno}",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 5th International Workshop on Non-Intrusive Load Monitoring, NILM 2020, co-located with ACM BuildSys 2020 ; Conference date: 18-11-2020",
year = "2020",
month = nov,
day = "18",
doi = "10.1145/3427771.3429390",
language = "English",
series = "NILM 2020 - Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring",
publisher = "Association for Computing Machinery, Inc",
pages = "89--93",
booktitle = "NILM 2020 - Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring",
}
