Comparison of supervised algorithms for automated data analysis in existing buildings

Abstract

Non-residential buildings are equipped with building automation systems (BAS). BAS provide a vast amount of building operation data. However, the analysis of this data is laborious and requires a scalable and automated process. The authors developed the BUDO schema for a standardized, object-oriented description of monitoring metadata. The translation process employs artificial intelligence to translate the properties of data streams (labels, time series) from BAS into the BUDO schema. The translated data is then analyzed using various applications to detect inefficiencies, such as incorrect placement of outdoor temperature sensors or a weekend shutdown that has not been properly implemented. The paper presents a process that automatically translates, structures and analyzes operational data to identify potential for energy savings. The OOM4ABDO project monitors 138 buildings to analyze and improve building operations, whose subset of data we are using here. In addition, we use an open source data set of about 70 buildings. We compare several feature extraction methods and classifiers on their ability to support each of the stages (label translation, time series translation, application). The algorithms used reach F1 scores of up to 98% for label translation, 90% for time series translation and 92% for applications.