Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/49960
Title: Modeling and optimization of building HVAC systems
Authors: Jin, Guang Yu
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Issue Date: 2011
Source: Jin, G. Y. (2011). Modeling and optimization of building HVAC systems. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This thesis presents the development of hybrid modeling methodologies for HVAC component static/steady-state models and dynamic/transient models, and the development and implementation of a model-based optimization approach for building heating, ventilating, and air conditioning (HVAC) systems, especially for the outbuilding section. Firstly, through component characteristic analysis, hybrid HVAC component models associated with cooling loads, operating variables and energy consumption characteristics for heat exchangers and energy consuming devices are established. All the model parameters can be derived from manufacturers’ specification data or on-site testing and measurement data. Secondly, the nonlinear constraint optimization problem for HVAC out-building section which consists of a refrigeration cycle and a condenser water loop is formulated by considering the system level and component level characteristics and interactions among all components and their associated variables. The optimization of both the refrigeration cycle and the condenser water loop is realized using a PSO based optimizer, with the target of minimizing the total power consumption of the HVAC system. Simulation studies of the proposed system optimization approach are conducted to compare the control accuracy, computation time and memory requirement of the proposed PSO based optimizer with those of the GA based optimizer using the same models. The results show that the system optimization approach using PSO based optimizer is able to achieve the same control accuracy yet requiring less computation time and memory compared to the system optimization approach using a GA based optimizer. Then the proposed hybrid model-based system optimization approach using a PSO based optimizer is implemented in the laboratorial centralized HVAC system to validate and evaluate the energy performance of the proposed method compare to traditional ones. The results of experimental tests show that the proposed method indeed improves the system performance significantly. The main contribution of this thesis is to propose hybrid modeling methodologies to predict the steady-state as well as the transient performance of the HVAC component, which is the prerequisite for model-based control and optimization; and to develop a general feasible model-based system optimization approach to systematically optimize the energy consumption of a HVAC system out-building section instead of optimizing its individual components.
URI: https://hdl.handle.net/10356/49960
DOI: 10.32657/10356/49960
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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