Subtask Gated Networks for Non-Intrusive Load Monitoring
Authors: Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, Wonjong Rhee1150-1157
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed methods on the two realworld datasets, REDD (Kolter and Johnson 2011) and UKDALE (Kelly and Knottenbelt 2014). |
| Researcher Affiliation | Collaboration | 1Encored Technologies, Seoul, Korea 2Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea 3Department of Transdisciplinary Studies, Seoul National University, Seoul, Korea |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described in this paper. |
| Open Datasets | Yes | Datasets We evaluate our proposed methods on the two realworld datasets, REDD (Kolter and Johnson 2011) and UKDALE (Kelly and Knottenbelt 2014). We only used the last week data that was published after preprocessing1. 1http://jack-kelly.com/files/neuralnilm/Neural NILM data.zip |
| Dataset Splits | No | The paper describes training and test set splits (e.g., 'We used the data of house 2 6 as the training set, and house 1 as the test set'), but does not explicitly mention a separate validation set split or how it was derived for reproducibility. |
| Hardware Specification | Yes | The DNN models are trained on NVIDIA GTX 1080Ti and implemented using Tensor Flow 1.8 package. |
| Software Dependencies | Yes | The DNN models are trained on NVIDIA GTX 1080Ti and implemented using Tensor Flow 1.8 package. |
| Experiment Setup | Yes | Our model has the following hyperparameters. The learning rate is 1.0 10 4, and the batch size is 16. Data was sliced with additional window size w=400 and output sequence length s=64 for REDD, w=200 and s=32 for UK-DALE. We used Adam optimizer (Kingma and Ba 2015) for training. |