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Integrated optimization based modeling and assessment for better building energy efficiency

Tahmasebi, Mostafa
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684775179972735

Abstract Details

2023, PhD, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
A substantial portion of buildings' total energy use is caused by heating, ventilation, and air conditioning (HVAC) systems. Data from the U.S. Energy Information Administration reports that buildings in the U.S. currently exhaust 72% of electricity produced and 55% of U.S. natural gas. In the U.S. the energy consumption of buildings exceeds that of transportation and other demand sectors. Of this energy, approximately half is used by heating and cooling systems. If energy usage trends continue at this pace, by 2025 buildings will turn into the largest users of energy worldwide. Developing methods and models that contribute to building energy savings is increasingly imperative for a sustainable future. Although most modern buildings today are equipped with advanced building automation systems (BAS) giving them the ability to collect a large amount of data, they still lack the embedded computational means and centralized solutions to operate in an optimal way. They face long term challenges too as it is estimated that around 30% of the total energy consumption in buildings is wasted due to lack of proper maintenance, aging equipment, and/or control issues. There is a significant need to explore how the latest computational methods can draw from available building data sources to perform modeling for optimization and energy efficiency. Fault detection requires model accuracy and appropriate thresholds. Machine learning-based energy models have proved to be efficient and accurate at this. This multi-level study introduces a comprehensive method to model, optimize and assess the performance of different components of HVAC systems. The proposed methods use performance data collected from real building components and are applicable to any existing system regardless of its complexity, configuration, or age. Development of accurate performance models was achieved by implementing various data driven modeling algorithms to datasets obtained from components performance data and comparing the accuracy of those models. The best performing models were then passed through a hyper parameter optimization algorithm to optimize for best model structure and increase prediction accuracy. The optimized data driven models capture the performance of HVAC systems with a high level of accuracy. Ultimately, these highly accurate models were deployed to assess the performance of HVAC systems, detect, and diagnose faults in them, and improve the security of building automation systems. To summarize, the following are the main objectives of this study: ? Develop and optimize accurate computational models to predict the performance of HVAC systems accurately ? Develop a method to detect and diagnose faults and maintenance issues in HVAC systems (lifelong assessment). ? Improve the system security, ensure the safety of power grid, and mitigate data intrusion attacks (cyber-attack) on BAS by detecting potential threats and restoring system operation when an attack occurs (cyber security assessment).
Nabil Nassif (Committee Chair)
Munir Nazzal, Ph.D. (Committee Member)
Hazem Elzarka, Ph.D. (Committee Member)
Pravin Bhiwapurkar (Committee Member)
150 p.
Engineering
Building Energy Modeling; Performance Optimization; Performance Assessment; Energy Efficiency; Decarbonization; Cyber Security

Recommended Citations

Citations

  • Tahmasebi, M. (2023). Integrated optimization based modeling and assessment for better building energy efficiency [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684775179972735

    APA Style (7th edition)

  • Tahmasebi, Mostafa. Integrated optimization based modeling and assessment for better building energy efficiency. 2023. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684775179972735.

    MLA Style (8th edition)

  • Tahmasebi, Mostafa. "Integrated optimization based modeling and assessment for better building energy efficiency." Doctoral dissertation, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1684775179972735

    Chicago Manual of Style (17th edition)

ucin1684775179972735
59
© 2023, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.
Release 3.2.12