Open Access
Kim, Yang-seon
Graduate Program:
Mechanical Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
October 30, 2014
Committee Members:
  • Dr Jelena Srebric, Dissertation Advisor
  • Hosam Kadry Fathy, Committee Chair
  • Fan Bill B Cheung, Committee Member
  • Chinemelu Jidenka Anumba, Special Member
  • building energy
  • occupancy
  • plug-load
  • building energy calibration
  • building energy simulation
High oil prices, diminishing natural resources, and global warming are causing developed countries to investigate ways to reduce their energy consumption. Commercial buildings account for nearly 18% of the national energy consumption of the United States, which produces approximately 12% of the annual global greenhouse gas emissions. Therefore, there is a pressing need to evaluate and understand the energy performances of commercial buildings during design and operation phases in an effort to increase their energy efficiency and conservation. However, some buildings fail to perform as their designers intended, in part because users do not or cannot properly operate the buildings or behave differently than designers expect. Therefore, a relationship between the occupancy patterns and building energy consumption is important. Prior studies have shown that occupant behavior, occupancy rate and occupancy presence/absence have a direct effect on building energy consumption. These studies revealed the importance of occupancy patterns not only for actual building energy consumption, but also for the accuracy of building energy simulations to predict the building energy consumption. However, it is hard to normalize the occupancy patterns to use as an input parameter for energy simulations. The main objective of this dissertation research is to improve the accuracy of the building energy simulations by accounting for occupancy patterns from data available in actual buildings. This objective resulted in a methodology based on metered electric data to derive occupancy schedules for input into building energy simulations. The development of this methodology was divided into three steps. First, this study quantified the effect of occupancy rates on the electricity consumption in office and campus buildings. Second, this study developed and validated a methodology to derive occupancy schedules from sub-metered electricity consumption for input into energy simulations. Third, this study examined the energy simulation accuracy with occupancy schedules derived from filtered hourly electricity consumption, when sub-metering is not available. To develop and demonstrate the methodology, this study used three buildings in Pennsylvania, USA, including Building 101 at Navy Yard in Philadelphia, as well as Forest Resources and Borland Buildings at the Penn State campus in State College. For these buildings, the study analyzed the occupancy rates and electricity consumption data. The correlation coefficients between the total electricity consumption and number of occupants was significant for the three studied buildings (R240%-70%). In the case study for Building 101, the plug-load consumption is more directly related to the occupancy rate (R270%-80%) when compared to the relationship between the total electricity consumption and the occupancy rates (R240%~60%). Nevertheless, typical buildings do not have detailed plug-load consumption data because of the costs associated with the instrumentation as well as the data collection and analysis. For buildings without sub-metering instrumentation, such as Forest Resources and Borland Buildings, this study found that the occupancy rates are significantly correlated to the fluctuations in the total energy consumption (R270%). Therefore, for the studied commercial buildings, the electricity consumption can be used to derive occupancy patterns for input into energy simulations. This study proposed a methodology to derive the occupancy schedules from the hourly electricity consumption data for input into building energy simulations. Most importantly, with these occupancy schedules, building energy simulations were successfully calibrated because when the simulation results are compared to the actual energy consumption, they satisfied the stringent accuracy requirements by the ASHARE Guideline 14 (CVRMSE < 15% for daily data).