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Novel Integrated Modeling and Optimization Technique for Better Commercial Buildings HVAC Systems Operation

Talib, Rand
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065969469013

Abstract Details

2021, PhD, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
The primary energy sources in commercial buildings are electricity that accounts for 61%, followed by 32% for natural gas. According to EIA, the heating, ventilation, and air condition systems account for about 25% of the total commercial building’s energy use in the US. Therefore, advanced modeling and optimization methods of the system components and operation offer great ways to reduce energy consumption. Since HVAC systems modeling is a characteristic and challenging process thus, while developing an HVAC system and component model, close attention should be given to the accuracy of the model structure, model parameters, and constraints. So, the final selected model can accurately deal with constraints, uncertainties, control the time-varying applications and handle a broad range of operating conditions. Also, the use of the optimization process to automate selecting the best model structure is crucial. Because every component is different, we cannot propose one model to fit that specified component in all systems. Choosing the best model structure is a time-consuming process. Here comes the optimization process role in automating the process of selecting the optimal model structure for each application. This research will introduce an innovative method of modeling and optimizing HVAC system operation to reduce the total energy consumption while improving the indoor thermal comfort level. The data-driven two-level optimization technique introduced in this research will utilize the use of real system performance data collected from the building automation systems (BAS) to create accurate component modeling and optimization process as the first level of optimization (MLO). Accurate component modeling techniques are crucial for the results accuracy of the process of optimization the HVAC systems performance. Lastly, artificial neural network (ANN) was chosen as the component modeling tool. The second level of optimization utilizes the whole system-level optimization (SLO). Genetic algorithm was selected as the optimization learning algorithm. Later, the two optimization levels will be integrated together to optimize the HVAC system operation. The proposed two-levels optimization technique has contributed to the field of modeling and optimization of HVAC systems through several new contributions. •
Nabil Nassif (Committee Chair)
Hazem Elzarka, Ph.D. (Committee Member)
Amanda Webb (Committee Member)
Munir Nazzal, Ph.D. (Committee Member)
Raj Manglik, Ph.D. (Committee Member)
232 p.
Engineering; Labor Economics; Theater
Implement the real-time zones occupancy sensor readings. This approach will crucially affect the zones' ventilation flow rates and zones minimum flow rates. •ANN; modeling; optimization; demand control; Genetic algorithm

Recommended Citations

Citations

  • Talib, R. (2021). Novel Integrated Modeling and Optimization Technique for Better Commercial Buildings HVAC Systems Operation [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065969469013

    APA Style (7th edition)

  • Talib, Rand. Novel Integrated Modeling and Optimization Technique for Better Commercial Buildings HVAC Systems Operation. 2021. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065969469013.

    MLA Style (8th edition)

  • Talib, Rand. "Novel Integrated Modeling and Optimization Technique for Better Commercial Buildings HVAC Systems Operation." Doctoral dissertation, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637065969469013

    Chicago Manual of Style (17th edition)

ucin1637065969469013
237
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This open access ETD is published by University of Cincinnati and OhioLINK.
Release 3.2.12