Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
Julien Leprince, Clayton Miller, Wim Zeiler
Abstract
Over the last decade, collecting massive volumes of data has been made all the more accessible, pushing the building sector to embrace data mining as a powerful tool for harvesting the potential of big data analytics. However repetitive challenges still persist emerging from the need for a common analytical frame, effective application- and insight-driven targeted data selection, as well as benchmarked-supported claims. This study addresses these concerns by putting forward a generic stepwise multidimensional data mining framework tailored to building data, leveraging the dimensional-structures of data cubes. Using the open Building Data Genome Project 2 set, composed of 3053 energy meters from 1636 buildings, we provide an online, open access, implementation illustration of our method applied to automated pattern identification. We define a 3-dimensional building cube echoing typical analytical frames of interest, namely, bottom-up, top-down and temporal drill-in approaches. Our results highlight the importance of application and insight driven mining for effective dimensional-frame targeting. Impactful visualizations were developed allowing practical human inspection, paving the path towards more interpretable analytics.