Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Dissertation_H_SHI.pdf (5.01 MB)

ETD Abstract Container

Abstract Header

Building Energy Efficiency Improvement and Thermal Comfort Diagnosis

Shi, Hongsen
http://rave.ohiolink.edu/etdc/view?acc_num=osu1555110595177379

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Food, Agricultural and Biological Engineering.
Thermal comfort is an important factor in designing high-quality buildings. The well-conditioned environment can keep occupants healthy and productive and ensure workplace safety. The heating, ventilation and air conditioning (HVAC) system plays an important role in providing and maintaining indoor thermal comfort for buildings. The faults in an HVAC system not only waste energy but also cause poor thermal comfort, building-related illnesses, or even safety accidents. This research adopted the model-based method to detect and diagnose the faults in a selected HVAC system. First, a simulation model of the case study building was created and validated based on both energy and thermal performance. Then, by comparing the indoor air temperatures between the simulation model and the real situation, three common types of faults in the HVAC system were detected for summer and winter, including: 1) control fault, 2) facility fault, and 3) design fault. In addition, the simulation fault was identified in the winter time. For each type of faults, the corresponding solutions were proposed, which will help building operators to locate and solve the faults quickly and accurately. As another important factor to designing high-quality buildings, building energy efficiency could reduce building’s energy consumption and their environmental footprint. To lower buildings’ significant energy consumption and high impacts on environmental sustainability, recent years have witnessed rapidly growing interests in efficient HVAC precooling control and optimization. However, due to the complex analytical modeling of building thermal transfer, rigorous mathematical optimization for HVAC precooling is highly challenging. As a result, progress on HVAC precooling optimization remains limited in the literature. One of the main contributions of this research is to overcome the aforementioned challenge and propose an accurate and tractable HVAC precooling optimization framework. The main results are two-fold: i) this research developed a Resistive-Capacitative (RC) -network-based analytical model for multi-zone HVAC precooling to minimize both total energy costs and peak load demand, and ii) this research showed that the HVAC precooling optimization problem based on the proposed RC network model admitted a convex approximation, which enabled an efficient optimization algorithm design. Further, extensive simulation studies were performed to verify the performance of the proposed mathematical model and algorithms. The numerical results indicated that compared with the five existing HVAC control strategies, the proposed algorithm consistently outperformed existing state-of-the-art approaches. In recent years, various HVAC control strategies have been proposed to reduce the buildings’ energy consumption and high environmental impacts. Due to different research designs, the performance of these strategies cannot be directly compared to determine the best approach for any real-world buildings. This research holistically evaluated the performance of 11 selected HVAC cooling strategies in the total cooling energy use, peak load demand, and related energy cost using a well-validated simulation model based on a case study building. The main findings are three-fold: i) the demand-limiting control methods (especially the exponential setpoint equation-based semi-analytical method) achieved the highest energy reduction ratios ranging from 9.80-10.48%; ii) precooling and extended precooling strategies reduced the peak load most significantly by 15.42% and 21.35%, respectively; and iii) the RC-network based precooling optimization proposed in this research achieved the largest electricity cost saving with the reduction ratios of 16.63% and 12.92% based on the two given price schedules.
Qian Chen (Advisor)
Jia Liu (Committee Member)
Sandra Metzler (Committee Member)
Lingying Zhao (Committee Member)
186 p.
Agricultural Engineering; Civil Engineering; Environmental Engineering; Sustainability
Heating, ventilation, and air-conditioning; HVAC; Building Simulation; Validation; Faults Detection and Diagnosis; Cooling; FDD; Peak Load; Energy Cost; Energy Efficiency Building; HVAC Precooling Scheduling; Distributed Optimization;

Recommended Citations

Citations

  • Shi, H. (2019). Building Energy Efficiency Improvement and Thermal Comfort Diagnosis [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555110595177379

    APA Style (7th edition)

  • Shi, Hongsen. Building Energy Efficiency Improvement and Thermal Comfort Diagnosis. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1555110595177379.

    MLA Style (8th edition)

  • Shi, Hongsen. "Building Energy Efficiency Improvement and Thermal Comfort Diagnosis." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555110595177379

    Chicago Manual of Style (17th edition)

osu1555110595177379
242
© 2019, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.