@mastersthesis {xiao2014dissertation, title = {A Data-Driven Approach for System Approximation and Set Point Optimization, with a Focus in HVAC Systems}, volume = {Ph.D.}, year = {2014}, month = {May}, pages = {140}, school = {University of Arizona}, type = {mastersDissertation}, address = {Tucson, AZ}, abstract = {

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{\textemdash}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.\ 

}, author = {Xiao Qin} }