Investigating occupancy profiles using convolutional neural networks

Abstract

In this paper, we implement a convolutional neural networks (CNN) based autoencoder to investigate occupancy profiles. We use the American time use survey (ATUS) data, which contains 191,558 schedules with binary occupancy information. Our results suggest that the trained filters provide an important insight of occupancy profiles (i.e., dominant and distinct patterns), and the latent space compresses the profiles with representative information.