Building energy models can accurately predict energy performance of buildings, if properly calibrated. This study developed and demonstrated a novel method to calibrate building energy models based on the occupancy and plug-load schedules derived from metered electric use data. Importantly, this study also proposed an occupancy assessment method applicable to resource limited situation when a building sub-metering system is not available. Furthermore, the developed method can facilitate accurate predictions of building energy performance without a requirement to simultaneously monitor energy use and occupancy rates. The method development process used data from an office type building (OB1), and further verified the method accuracy with data from two campus buildings (CB1 and CB2). The developed method is novel because it considers interactions of the validated modeled occupancy patterns, processed electricity use patterns, and the calibrated building energy model results at the hourly level. This approach allows addressing limitations in the current studies that are not fully capable of modeling occupancy patterns, electricity use patterns, and calibrated building energy models with this level of granularity. The accuracy of the building energy modeling results increases with the derived occupancy schedules and plug-loads. Specifically, the Coefficient of Variation Root Mean Square Error (CVRMSE) of OB1 building energy modeling results improved from 21% to 12% compared to the modeling results obtained with default schedules. The results from case study buildings CB1 and CB2 show that the accuracy of modeling results increased as the hourly electricity CVRMSE decreased from 128% to 31% and from 156% to 16%, respectively. These improvements are significant, while the
This study demonstrates that capital availability needs to be considered while developing retrofit measures. Specifically, this study established a methodology using building energy simulations to determine optimal retrofit options over a range of NIST greenhouse gas pricing projections, full and half-price measure costs, and capital availability ranging from $1/ft 2-yr ($10.76/m 2-yr) to $100/ft 2-yr ($1076.39/m 2-yr), representing no capital constraint. The demonstration considers a sub-metered office building in Philadelphia with central heating and cooling equipment nearing replacement. When capital is restrained, measure installation occurs over several years, reducing energy and cost savings over the investment lifetime. This effect is as significant as the greenhouse gas price. Furthermore, changing measure installation order matters most when capital availability
This study uses cluster analysis to examine simulated energy consumption of 134 U.S. LEED NC office buildings to classify buildings into high, medium, and low energy use intensity clusters. The analysis uses energy simulation results from the LEED database, as a comparably large data set of energy end uses from sub-meter data does not yet exist. The difference between the low energy use cluster and other clusters is explained mostly by lower process loads and lower heating energy intensity, and partly by lower intensities of other HVAC related end-uses. Lighting energy use shows the least variation between clusters. The lower heating energy intensity in the low intensity cluster is largely explained by lower roof U-values, lower window-to-wall ratio, and smaller building size. Unregulated process loads are the most significant contributor to total building energy use, accounting for 36%, 33%, and 31% of the energy use in the high, medium, and low intensity clusters, respectively. This analysis provides a quantitative evaluation of the large difference in energy intensities in high-performance office buildings, showing that these buildings are dominated by internal loads, especially unregulated process loads.
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