Name
Abstract | ||
|---|---|---|
ABSTRACTThe growing request for eco-feedback and smart living concepts accelerated the development of Non-Intrusive Load Monitoring (NILM) algorithms during the last decade. Comparing and evaluating these algorithms still remains challenging due to the absence of a common benchmark datasets, and missing best practises for their application. Despite the fact that multiple datasets were recorded for the purpose of comparing NILM algorithms, many researchers still have to record their own dataset in order to meet the requirements of their specific application. Adding ground truth labels to these datasets is a cumbersome and time consuming process as it requires an expert to visually inspect all the data manually. Therefore, we propose the Annoticity inspection and labeling tool which simplifies the process of visualizing and labeling of electricity data. We use an event detector based on the log likelihood ratio test which achieved an F1 score of 90.07 % in our experiments. Preliminary results indicate that the effort of generating event labels is reduced by 80.35 % using our tool. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3427771.3427844 | SENSYS |
DocType | Citations | PageRank |
Conference | 1 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Völker | 1 | 1 | 2.41 |
| Marc Pfeifer | 2 | 1 | 1.40 |
| Philipp Scholl | 3 | 54 | 18.12 |
| Bernd Becker | 4 | 855 | 73.74 |