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dc.contributor.advisorLysecky, Susanen_US
dc.contributor.advisorSprinkle, Jonathanen_US
dc.contributor.authorQin, Xiao
dc.creatorQin, Xiaoen_US
dc.date.accessioned2014-06-03T21:47:58Z
dc.date.available2014-06-03T21:47:58Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10150/318828
dc.description.abstractDynamically determining input signals to a complex system, to increase performance 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 measure state variables, making these solutions difficult to implement or to scale down in cost. This work introduces methods to approximate complex system behavior prediction 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 inexpensive, 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.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectData-drivenen_US
dc.subjectHVACen_US
dc.subjectOptimizationen_US
dc.subjectSystem Modelingen_US
dc.subjectElectrical & Computer Engineeringen_US
dc.subjectControlen_US
dc.titleA Data-Driven Approach for System Approximation and Set Point Optimization, with a Focus in HVAC Systemsen_US
dc.typetexten
dc.typeElectronic Dissertationen
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberLysecky, Susanen_US
dc.contributor.committeememberSprinkle, Jonathanen_US
dc.contributor.committeememberSanfelice, Ricardoen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-04-26T07:37:58Z
html.description.abstractDynamically determining input signals to a complex system, to increase performance 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 measure state variables, making these solutions difficult to implement or to scale down in cost. This work introduces methods to approximate complex system behavior prediction 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 inexpensive, 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.


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