Name
Abstract | ||
|---|---|---|
Non Intrusive Load Monitoring is the field that encompasses energy disaggregation and appliance detection. In recent years, Deep Neural Networks have improved the classification performance, using the standard data representation that most datasets provide; that being low-frequency or high-frequency data. In this paper, we explore the NILM problem from the scope of transfer learning. We propose a way of changing the feature space with the use of an image representation of the low-frequency data from UK-Dale and REDD datasets and the pretrained Convolutional Neural Network VGG16. We then train some basic classifiers and use the metric F1 score to test the performance of this representation. Multiple tests are performed to test the adaptability of the models to unseen houses and different datasets. We find that the performance is on par and in some cases outperforms that of popular deep NN algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20257-6_16 | Communications in Computer and Information Science |
Keywords | Field | DocType |
NILM,Energy disaggregation,Transfer learning,Artificial neural networks | Adaptability,F1 score,Feature vector,External Data Representation,Pattern recognition,Convolutional neural network,Computer science,Image representation,Transfer of learning,Artificial intelligence,Artificial neural network | Conference |
Volume | ISSN | Citations |
1000 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lamprini Kyrkou | 1 | 0 | 0.34 |
| Christoforos Nalmpantis | 2 | 3 | 2.43 |
| Dimitris Vrakas | 3 | 251 | 23.98 |