Improved Appliance Classification in Non-Intrusive Load Monitoring using Weighted Recurrence Plots and Convolutional Neural Networks.

This repository is the official implementation of Improved Appliance Classification in Non-Intrusive Load Monitoring using Weighted Recurrence Plots and Convolutional Neural Networks.. The paper present a recurrence graph feature representation that gives a few more values (WRG) instead of the binary output, which improves the robustness of appliance recognition. The WRG representation for activation current and voltage not only enhances appliance classification performance but also guarantees the appliance feature's uniqueness, which is highly desirable for generalization purposes.

We further, present a novel pre-processing procedure for extracting steady-state cycle activation current from current and voltage measurements. The pre-processing method ensures that the selected activation current is not a transient signal. Experimental evaluation in the three sub-metered datasets shows that the proposed WRG feature representation offers superior performance when compared to the V-I based image feature. We conduct evaluations on three sub-metered public datasets and comparing with the V-I image, which is its most direct competitor. We also conduct an empirical investigation on how different parameters of the proposed WRG influence classification performance

Requirements

• python
• numpy
• matplotlib
• tqdm
• torch
• sklearn
• seaborn
• soundfile

Usage

1. Preprocess the data for a specific datasets
2. To replicate experiment results you can run the run_experiments.py code in the experiments directory.
3. The script used to analyse results and produce visualisation presented in this paper can be found in notebook directory
   • ResultsAnalysis notebook provide scripts for results and error analysis.
   • Visualizepaper notebook provide scripts for reproducing most of the figure used in this paper.

If you find this tool useful and use it (or parts of it), we ask you to cite the following work in your publications:

Faustine, A.; Pereira, L. Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks. Energies 2020, 13, 3374.

tex @article{Faustine2020, doi = {10.3390/en13133374}, url = {https://doi.org/10.3390/en13133374}, year = {2020}, month = jul, publisher = {{MDPI} {AG}}, volume = {13}, number = {13}, pages = {3374}, author = {Anthony Faustine and Lucas Pereira}, title = {Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks}, journal = {Energies} }

Results

Our model achieves the following performance on the three sub-metered data-set:

Dataset	V-I baseline	Proposed WRG
COOLL	98.95	99.86
WHITED	89.63	97.23
PLAID	84.75	88.53