/opemax_vitraj

This is the code for "Performance Evaluation of Appliance Identification Methods under Open-Set Conditions in Non-Intrusive Load Monitoring".

Primary LanguagePython

opemax_vitraj

This is the code for "Performance Evaluation of Appliance Identification Methods under Open-Set Conditions in Non-Intrusive Load Monitoring".

Requirements

pip install torch torchvision
pip install scikit-learn
pip install numpy
pip install libmr

Data

The training and testing data can be found in 'data/dataset-name/hsv_vi.npy' and 'data/dataset-name/labels.npy'.

To see the RGB image of the HSV V-I trajectory, please see 'notebook/plot_hsv_vi_traj.ipynb'.

Training

This is a PyTorch implementation of the project, run following command to train inital model, test will run after training ends:

Single Unknown Appliance Tests

python main.py --dataset plaid --u_class '0'

Multiple Unknown Appliance Tests

python main.py --dataset cooll --u_class '0_4_1'

Results

The trained model state dict and results will be saved under 'checkpoints/dataset-name/unknown_class/'. We have uploaded some results for review.

To check the results, please see 'check_results.py'.

Acknowledgement

During the implementation we base our code mostly on the PLAID by Jingkun Gao, we are also inspired by the Open-Set-Recognition implementation by Xu Ma and MLCFCD from Anthony Faustine. Many thanks to these authors for their great work!