02509nas a2200133 4500008004100000245010900041210006900150260004300219300000800262490001000270520195700280100001402237856012402251 2014 eng d00aA Data-Driven Approach for System Approximation and Set Point Optimization, with a Focus in HVAC Systems0 aDataDriven Approach for System Approximation and Set Point Optim aTucson, AZbUniversity of ArizonacMay a1400 vPh.D.3 a

Dynamically determining input signals to a complex system, to increase perfor- mance and/or reduce cost, is a difficult task unless users are provided with feedback on the consequences of different input decisions. For example, users self-determine the set point schedule (i.e. temperature thresholds) of their HVAC system, without an ability to predict cost—they select only comfort. Users are unable to optimize the set point schedule with respect to cost because the cost feedback is provided at billing-cycle intervals. To provide rapid feedback (such as expected monthly/daily cost), mechanisms for system monitoring, data-driven modeling, simulation, and optimization are needed. Techniques from the literature require in-depth knowledge in the domain, and/or significant investment in infrastructure or equipment to mea- sure state variables, making these solutions difficult to implement or to scale down in cost.

This work introduces methods to approximate complex system behavior predic- tion and optimization, based on dynamic data obtained from inexpensive sensors. Unlike many existing approaches, we do not extract an exact model to capture every detail of the system; rather, we develop an approximated model with key predictive characteristics. Such a model makes estimation and prediction available to users who can then make informed decisions; alternatively, these estimates are made available as an input to an optimization tool to automatically provide pareto- optimized set points. Moreover, the approximation nature of this model makes the determination of the prediction and optimization parameters computationally in- expensive, adaptive to system or environment change, and suitable for embedded system implementation. Effectiveness of these methods is first demonstrated on an HVAC system methodology, and then extended to a variety of complex system applications. 

1 aQin, Xiao uhttps://csl.arizona.edu/content/data-driven-approach-system-approximation-and-set-point-optimization-focus-hvac-systems00460nas a2200133 4500008004100000245009900041210006900140300000600209490000700215100001400222700001900236700002300255856004800278 2014 eng d00aA Data-Driven Linear Approximation of HVAC Utilization for Predictive Control and Optimization0 aDataDriven Linear Approximation of HVAC Utilization for Predicti a10 v991 aQin, Xiao1 aLysecky, Susan1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1109/TCST.2014.2332873