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Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
| Content Provider | MDPI |
|---|---|
| Author | Weng, Liguo Zhang, Xiaodong Qian, Junhao Xia, Min Xu, Yiqing Wang, Ke |
| Copyright Year | 2020 |
| Description | Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances. |
| Starting Page | 9132 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10249132 |
| Journal | Applied Sciences |
| Issue Number | 24 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-21 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Load Disaggregation Multi-scale Attention Mechanism Residual Network |
| Content Type | Text |
| Resource Type | Article |
