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Title: HVAC control using support vector regression models
Keywords: HVAC system, Support Vector Regression, Inverse Control, Nonlinear Model Predictive Control,Temperature, Relative Humidity
Issue Date: 21-Jan-2004
Citation: XI XUECHENG (2004-01-21). HVAC control using support vector regression models. ScholarBank@NUS Repository.
Abstract: Some industrial and scientific processes require simultaneous and accurate control of temperature and relative humidity. HVAC system is a typical nonlinear system. Support vector regression (SVR) is a type of model that is optimized so that prediction error and model complexity are simultaneously minimized. In this project, support vector regression is used to build the inverse and forward dynamic models of a HVAC system. A SVR inverse controller and a SVR nonlinear model predictive controller are designed based on the SVR inverse model and the SVR forward model respectively. Experimental results have shown that both the room temperature and the room relative humidity are accurately controlled to their desired values respectively within the system operating range. The control performance is quite satisfactory in terms of reference tracking ability, steady error and amplitude of overshooting.
Appears in Collections:Master's Theses (Open)

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