Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements

• DOI: 10.48550/arxiv.1802.06963
• Publication Date: 2018-01-01
• Authors: Karim Said Barsim; Lukas Mauch; Bin Yang
• Keywords: Identification; Plug-in

The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated plug-wise metering. Therefore, has received dedicated studies with various electric signatures, classification models, evaluation datasets. In this paper, we propose neural network ensembles approach to address using high resolution measurements. models are trained on raw current voltage waveforms, thus, eliminating need for well engineered signatures. We evaluate proposed model publicly available dataset 55 residential buildings, 11 categories, over 1000 further study stability respect training dataset, sampling frequency, variations steady-state operation appliances.