<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A detailed knowledge of the energy consumption and activation status of the electrical appliances in a house is beneficial for both the user and the energy supplier, improving energy awareness and allowing the implementation of consumption management policies through demand response techniques. Monitoring the consumption of individual appliances is certainly expensive and difficult to implement technically on a large scale, so non-intrusive monitoring techniques have been developed that 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 total energy consumption with high temporal resolution. In this work we use a deep learning method to disaggregate the low frequency energy signal generated directly by the new generation smart meters deployed in Italy, without the need of additional specific hardware. The performances obtained on two reference datasets are promising and demonstrate the applicability of the proposed approach.</p>
</abstract>

A detailed knowledge of the energy consumption and activation status of the electrical appliances in a house is beneficial for both the user and the energy supplier, improving energy awareness and allowing the implementation of consumption management policies through demand response techniques. Monitoring the consumption of individual appliances is certainly expensive and difficult to implement technically on a large scale, so non-intrusive monitoring techniques have been developed that 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 total energy consumption with high temporal resolution. In this work we use a deep learning method to disaggregate the low frequency energy signal generated directly by the new generation smart meters deployed in Italy, without the need of additional specific hardware. The performances obtained on two reference datasets are promising and demonstrate the applicability of the proposed approach.

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

Department of Mathematics and Computer Science

Communications in Applied and Industrial Mathematics

This work is licensed under the Creative Commons Attribution 4.0 International License.

A detailed knowledge of the energy consumption and activation status of the electrical appliances in a house is beneficial for both the user and the energy...

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

Data publikacji: 11 paź 2022

Zakres stron: 39 - 56

Otrzymano: 02 maj 2022

Przyjęty: 16 wrz 2022

DOI: https://doi.org/10.2478/caim-2022-0004

Słowa kluczowe
energy disaggregation, non-intrusive load monitoring, NILM, deep learning, smart meter, Chain 2

© 2022 Marco Manolo Manca et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 11 paź 2022

Zakres stron: 39 - 56

Otrzymano: 02 maj 2022

Przyjęty: 16 wrz 2022

DOI: https://doi.org/10.2478/caim-2022-0004

Słowa kluczoweenergy disaggregation, non-intrusive load monitoring, NILM, deep learning, smart meter, Chain 2

© 2022 Marco Manolo Manca et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
energy disaggregation, non-intrusive load monitoring, NILM, deep learning, smart meter, Chain 2