Deep learning based non-intrusive load monitoring with low resolution data from smart meters

A detailed knowledge of the energy consumption and activation status of the electrical appliances in a house is beneficialfor both the user and the energy supplier, improving energy awareness and allowing the implementation of consumptionmanagement policies through demand response techniques. Monitoring the consumption of individual appliances is certainlyexpensive and difficult to implement technically on a large scale, so non-intrusive monitoring techniques have been developedthat allow the consumption of appliances to be derived from the sole measurement of the aggregate consumption of a house.However, these methodologies often require additional hardware to be installed in the domestic system to measure totalenergy consumption with high temporal resolution. In this work we use a deep learning method to disaggregate the lowfrequency energy signal generated directly by the new generation smart meters deployed in Italy, without the need ofadditional specific hardware. The performances obtained on two reference datasets are promising and demonstrate theapplicability of the proposed approach.

@Article{MM22b,
  author       = {Manca, M. and Massidda, L.},
  title        = {Deep learning based non-intrusive load monitoring with low resolution data from smart meters},
  journal      = {Communications in Applied and Industrial Mathematics},
  number       = {1},
  volume       = {13},
  pages        = {39--56},
  month        = {september},
  year         = {2022},
  publisher    = {SIMAI},
  keywords     = {energy disaggregation, non-intrusive load monitoring, NILM, deep learning, smart meter, Chain 2},
  doi          = {10.2478/caim-2022-0004},
  url          = {https://publications.crs4.it/pubdocs/2022/MM22b},
}