SCAFE: Automated simultaneous clustering and non-linear feature extraction of building energy profiles

Abstract

Increasing amounts of energy consumption data available to researchers in recent years make it easier to analyze the global energy demands. The analysis is important because buildings consume roughly 40% of global energy. One of the applications of data-driven analysis is identifying buildings that follow similar energy consumption patterns. Despite the number of available unsupervised machine learning algorithms, the user is still required to choose features, typically resorting to hourly data. In this paper, we propose we present SCAFE, an end-to-end architecture for Simultaneous Clustering And non-linear Feature Extraction. The method consists of first transforming the raw energy profiles that are represented as time-series into three channel images, using Gramian Summation, Gramian Difference and Markov Transition Fields. Then, we map the images to the latent space with the use of an autoencoder. Finally, we perform clustering in the latent space, simultaneously obtaining clusters and features of the dataset. We demonstrate SCAFE on the UTexas dataset, showing similar performance to standard k-means clustering while in addition also extracting natural features of the data in the latent space.