Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements
This addresses the problem of identifying electrical appliances from consumption profiles for applications like Non-Intrusive Load Monitoring, but it is incremental as it builds on existing classification models.
The paper tackled the appliance identification problem by proposing a neural network ensembles approach that uses raw current and voltage waveforms, eliminating the need for engineered signatures, and achieved evaluation on a dataset from 55 buildings with 11 appliance categories and over 1000 measurements.
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, appliance identification has received dedicated studies with various electric appliance signatures, classification models, and evaluation datasets. In this paper, we propose a neural network ensembles approach to address this problem using high resolution measurements. The models are trained on the raw current and voltage waveforms, and thus, eliminating the need for well engineered appliance signatures. We evaluate the proposed model on a publicly available appliance dataset from 55 residential buildings, 11 appliance categories, and over 1000 measurements. We further study the stability of the trained models with respect to training dataset, sampling frequency, and variations in the steady-state operation of appliances.