On analyzing the household energy consumption detection for citizen behavioral analysis carbon footprint awareness by deep residual networks
Arijit Ukil, Antonio J. Jara, Leandro Marín · Conference on Information and Knowledge Management · 2020
With the exponential growth of household activities particularly due to the lock-down in COVID-19 pandemic as well as with the usual trend of amplified use of energy consuming appliances, household energy usages are becoming extremely high Consequently, high energy consumption pattern results in severe increase of air pollution and carbon footprint Carbon footprint is mainly caused by the greenhouse gases while burning of fossil fuels for producing different forms of energy In order to restrict the carbon footprint, one of the approaches is to analyze the citizen behavioral pattern by detecting the household appliances We propose deep neural network based supervised learning algorithm that is capable of classifying the household appliances from energy consumption data More specifically, we use deep residual networks (ResNet) where learning of the residual functions makes the trained model more robust by transforming the representation learning problem to residual learning problem Our empirical study on publicly available relevant datasets from UCR timeseries archive demonstrates significantly better and consistent performance over baseline algorithms and state-of-the-art methods © 2020 CEUR-WS All rights reserved