PhD Dissertation

Abstract: This research studies the estimation of peak electric demand savings as a result of implementing Continuous Commissioning® (CC®) measures in commercial buildings, by using energy modeling, model calibration, and data analysis.

The investigation involves developing energy models, and analyzing peak electric demand data generated by each model, in comparison with a baseline calibrated model. The study is based on a community college campus located in Fort Worth, TX, which consists of four main buildings with a total conditioned floor area of 932,000 sq.ft. The campus underwent a CC project between 2014 and 2015, with a focus on HVAC system operational control adjustments, which resulted in an average of 34% savings in energy consumption and peak demand. The total peak demand reduction estimated by the energy models developed in this study is 30%.

The peak demand of the campus was examined before and after the implementation of the Continuous Commissioning project. This research simulates the peak demand savings as a result of applying all CC measures, as well as investigates the independent impact of Continuous Commissioning (CC) measures on peak electric demand, to help prioritize CC measures based on peak demand savings along with the usually-assessed energy consumption savings.

The energy modeling is based on a combination of physics-based and black-box modeling techniques, by utilizing EnergyPlus™ and WinAM; an Energy Systems Laboratory’s internally-developed software based on the ASHRAE Simplified Energy Analysis Procedure (SEAP). The modeling framework used in this research is found to be capable of accurately simulating the peak demand savings by developing calibrated energy models based on energy consumption, and meeting the ASHRAE Guideline 14 energy modeling calibration criterion.

It is found that some CC measures generate significant peak demand savings, while other measures do not have a tangible impact on peak demand, even though they generate energy consumption savings. This conclusion will help energy researchers and engineers prioritize and analyze different CC measures based on the economics of the facility and the needs of its owners.