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Non-intrusive load disaggregation via a fully convolutional neural network: improving the accuracy on unseen household

Luca Massidda, Marino Marrocu, Simone Manca
2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), Volume 1, page 317-322 - september 2020
The application of load disaggregation techniques based on neural networks is often limited to users included in the training dataset. A methodology based on techniques typical of the semantic segmentation of images has been proposed for this task, which allows to obtain a high accuracy and good generalization. We introduce here a novel data augmentation technique for improving forecasts for unmonitored houses that does not require any interaction with the user, nor further measurements of consumption of household appliances.

BibTex references

@InProceedings{MMM20a,
  author       = {Massidda, L. and Marrocu, M. and Manca, S.},
  title        = {Non-intrusive load disaggregation via a fully convolutional neural network: improving the accuracy on unseen household},
  booktitle    = {2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)},
  volume       = {1},
  pages        = {317-322},
  month        = {september},
  year         = {2020},
  editor       = {IEEE},
  publisher    = {IEEE},
  organization = {IEEE},
  keywords     = {energy disaggregation, non-intrusive load monitoring, convolutional neural network, deep learning},
  doi          = {10.1109/IESES45645.2020.9210661},
  url          = {http://publications.crs4.it/pubdocs/2020/MMM20a},
}

Other publications in the database

» Luca Massidda
» Marino Marrocu
» Simone Manca