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| Title | A Survey On The Non-Intrusive Load Monitoring; [非侵入式负荷监测综述] |
|---|---|
| ID_Doc | 5346 |
| Authors | Deng X.-P.; Zhang G.-Q.; Wei Q.-L.; Peng W.; Li C.-D. |
| Year | 2022 |
| Published | Zidonghua Xuebao/Acta Automatica Sinica, 48, 3 |
| DOI | http://dx.doi.org/10.16383/j.aas.c200270 |
| Abstract | Non-intrusive load monitoring can realize the identification of individual electrical equipment and its working state by analyzing and processing the aggregated load data from electricity meters, which can be widely used in building energy conservation, smart cities, smart grids, etc. With the large-scale deployment of smart meters and the widespread application of various machine learning algorithms, non-intrusive load monitoring has aroused the common concern of academia and industry in recent years. This article reviews the research on non-intrusive load monitoring. First, the mathematical model and basic framework of non-intrusive load monitoring are refined, and then we separately summarize the data collection and pre-processing process, load disaggregation models and algorithms, data sets and evaluation metrics used in non-intrusive load monitoring. Finally, some challenges that the current research are confronted with are analyzed and we give some views on the future research. Copyright ©2022 Acta Automatica Sinica. All rights reserved. |
| Author Keywords | Deep learning; Feature extraction; Hidden Markov model; Load disaggregation; Non-intrusive load monitoring (NILM) |
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