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Interactive analytics for smart cities infrastructures

Infrastructures in smart cities are complex systems, more than the sum of their parts, and operate through a multitude of individual and collective decisions. These systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles, at all scales and levels. As a result, facility managers for such systems are demanding more control over the system operation. They prefer data-driven insights into performance and usage across their portfolios to make more effective decisions. Such insights help determine, if the system is operating efficiently and enable the facilities manager/system operator to investigate areas for improvement and evaluate upgrades. In this paper we describe one such system, which uses data-driven control-oriented models to build a data intelligence layer for the underlying physical infrastructure. Our system provides interactive analytics for the operator by answering queries and making recommendations about the systems operation. We extend our previous work with using regression trees ensembles for predictive modeling of these large and 'messy' systems, and show how tree based models can be converted into a knowledge discovery database. We present preliminary results for this system using data from a large office building.