Author ORCID Identifier

https://orcid.org/0000-0001-7352-273X

Semester

Summer

Date of Graduation

2023

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Industrial and Managements Systems Engineering

Committee Chair

Bhaskaran Gopalakrishnan

Committee Member

Zhichao Liu

Committee Member

Hailin Li

Abstract

The urgent need for improving building energy efficiency in response to global warming and environmental sustainability has highlighted the importance of practical techniques to optimize energy performance. The study utilizes the eQUEST simulation engine and Energy Star® Portfolio Manager to evaluate retrofit and design parameters and conduct a sensitivity analysis to explore the impact of different parameters on building energy performance. The study develops building energy models in eQUEST using data from two fully operational Distribution centers. It is calibrated using the Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method, meeting the calibration criteria specified by the ASHRAE Guideline 14, Federal Energy Management Program (FEMP), and International Performance Measurement and Verification Protocol (IPMVP). A fractional factorial analysis is conducted on eight energy efficiency measures targeting the highest energy contributors to the building, and the results are benchmarked using Energy Star® Portfolio Manager to obtain benchmarking scores ranging from 1-100.

Throughout the day, occupancy levels, lighting, plug loads, and other equipment are used in various spaces; a simulation model for the data input sheet is created in Microsoft Excel® using the Visual Basic Application (VBA)® and incorporating uncertainty defined by a uniform distribution. Subsequently, Python is used to conduct sensitivity analysis to evaluate various parameters and reduce energy consumption while observing changes in the benchmarked score for the buildings, with coefficient significance determined by its magnitude relative to the sum of all coefficients. In the analyzed patterns for Building A, efficient lighting was the most influential parameter for energy saving (16.93%), benchmarking score (9.11%), and carbon emission saving (18.71%), whereas for Building B, HVAC Efficiency (23.42%) had the most influence on energy saving and Demand controlled ventilation and economizer (7.02%) on Benchmarking score. State grid emissions and natural gas emission factors affect carbon savings, while site energy efficiency projects impact energy savings; however, benchmarking scores may not align due to source-site ratios and building characteristics. This study's synergistic analysis emphasizes the importance of benchmarking scores and energy efficiency measures in promoting sustainable building practices, reducing energy consumption, and improving the market competitiveness of buildings.

Embargo Reason

Publication Pending

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