Anthony Faustine, Lucas Pereira and Christoph Klemenjak “Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring,” IEEE Transactions on Smart Grid vol. 12, no. 1, pp. 398-406, Jan. 2021, doi: 10.1109/TSG.2020.3010621.
Anthony Faustine 
Lucas Pereira
Christoph Klemenjak
Abstract
To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load Monitoring (NILM) research, as researchers and practitioners are forced to invest a considerable amount of time in this task. This paper proposes adaptive weighted recurrence graph blocks (AWRG) for appliance feature representation in event-based NILM. An AWRG block can be combined with traditional deep neural network architectures such as Convolutional Neural Networks for appliance recognition. Our approach transforms one cycle per activation current into an weighted recurrence graph and treats the associated hyper-parameters as learn-able parameters. We evaluate our technique on two energy datasets, the industrial dataset LILACD and the residential PLAID dataset. The outcome of our experiments shows that transforming current waveforms into weighted recurrence graphs provides a better feature representation and thus, improved classification results. It is concluded that our approach can guarantee uniqueness of appliance features, leading to enhanced generalisation abilities when compared to the widely researched V-I image features. Furthermore, we show that the initialisation parameters of the AWRG’s have a significant impact on the performance and training convergence.
Figure
Figure 1: Simulation results
Citation
@ARTICLE{9144492,
author={Faustine, Anthony and Pereira, Lucas and Klemenjak, Christoph},
journal={IEEE Transactions on Smart Grid},
title={Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring},
year={2021},
volume={12},
number={1},
pages={398-406},
keywords={Feature extraction;Monitoring;Aggregates;Neural networks;Current measurement;Voltage measurement;Energy consumption;Non-intrusive load monitoring;load disaggregation;appliance recognition;weighted recurrence graphs;recurrence plots;V-I trajectories;convolutional neural networks;deep neural networks},
doi={10.1109/TSG.2020.3010621}}