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

Massidda, Luca; Marrocu, Marino; Manca, Simone

doi:10.1109/ieses45645.2020.9210661

Cited by 2 publications

(1 citation statement)

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“…The model proved to be very effective in disaggregating the loads of a house even in the case, more interesting from the application point of view, in which the neural network is applied to the load of a house not present in the dataset used for the training of the model. We have also experimented with network ensembling techniques to further improve the already good accuracy achieved by the network [35]. One of the peculiarities of the proposed model is the use of data with a reduced sampling rate (1 min) for both training and testing, in order to allow a possible application of the technique to measurements coming directly from the smart meter without the use of dedicated monitoring hardware.…”

Section: Introductionmentioning

confidence: 99%

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

Manca

Massidda

2022

Communications in Applied and Industrial Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%