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Neural Fourier Energy Disaggregation
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
doi:10.3390/s22020473
pmid:35062434
pmcid:PMC8779842
fatcat:3xbu4gql2nc4xlfnwbt4swnlf4