Neural Fourier Energy Disaggregation

Christoforos Nalmpantis, Nikolaos Virtsionis Gkalinikis, Dimitris Vrakas
2022 Sensors  
Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models are usually computationally expensive and they have large storage requirements. Reducing the computational cost and the size of a neural network, without trading off any performance is not a trivial task. This paper suggests a novel neural
more » ... ure that has less learning parameters, smaller size and fast inference time without trading off performance. The proposed architecture performs on par with two popular strong baseline models. The key characteristic is the Fourier transformation which has no learning parameters and it can be computed efficiently.
doi:10.3390/s22020473 pmid:35062434 pmcid:PMC8779842 fatcat:3xbu4gql2nc4xlfnwbt4swnlf4