antoniosudoso / attention-nilm

An Attention-based Deep Neural Network for Non-Intrusive Load Monitoring

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

This repository provides the implementation of LDwA (Load Disaggregation with Attention) described in the paper:

V. Piccialli and A. M. Sudoso, "Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network", Energies 2021, 14, 847. https://doi.org/10.3390/en14040847.

The source code is written in Python and the DNN is implemented with Tensorflow.

Citation export:

@Article{en14040847,
AUTHOR = {Piccialli, Veronica and Sudoso, Antonio M.},
TITLE = {Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network},
JOURNAL = {Energies},
VOLUME = {14},
YEAR = {2021},
NUMBER = {4},
ARTICLE-NUMBER = {847},
URL = {https://www.mdpi.com/1996-1073/14/4/847},
ISSN = {1996-1073},
DOI = {10.3390/en14040847}
}

Related Work

Our new paper entitled Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring has been published in IEEE Transactions on Smart Grid.

M. Balletti, V. Piccialli and A. M. Sudoso, "Mixed-Integer Nonlinear Programming for State-Based Non-Intrusive Load Monitoring", IEEE Transactions on Smart Grid 2022, vol. 13, no. 4, pp. 3301-3314. https://doi.org/10.1109/TSG.2022.3152147.

See the source code here.