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This paper is the first two that report on the results of ASHRAE Research Project RP-1468. This paper presents an overview of the projectandthe three volumes of documentation included in the final report (Clayton et al. 2013). The purpose of the project was to produce reference models and guidelines for developing software to automate thermal model creation fromabuilding informationmodelintoabuilding energy simulation (BES) for energy simulations. This paper describes the methods of the research and presents highlights of the guidelines. The variety of uses for energy simulation in the building design process leads to a differentiation of several different kinds of thermal models that may be extracted from a BIM. A Series 2, Level 1 model that addresses whole-building energy use at schematic stages of design is developed into a set of guidelines. A recommendation is to use the centerlines of enclosure elements to determine areas of thermal surfaces. Various recommendations are made for resolving curved surfaces, shading elements, and site elements. The guidelines are supported by detailed sensitivity studies and analysis, presented in the final report (Clayton et al. 2013). The investigations led to identification of future work to improve energy simulation tools and BIM tools. [ABSTRACT FROM AUTHOR]

Energy modeling and optimization studies can facilitate the design of cost-effective, low-energy buildings. However, this process inevitably involves uncertainties such as predicting occupant behavior, future climate, and econometric parameters. As presently practiced, energy modelers typically do not quantify the implications of these unknowns into performance outcomes. This paper describes an energy modeling approach to quantify economic risk and better inform decision makers of the economic feasibility of a project. The proposed methodology suggests howeconomic uncertainty can be quantified within an optimization framework. This approach improves modeling outcomes by factoring in the effect of variability in assumptions and improves confidence in simulation results. The methodology is demonstrated using a net zero energy commercial office building case study located in London, ON, Canada. [ABSTRACT FROM AUTHOR]

This paper introduces and provides results from a rigorous, scientific testing methodology that allows pure building model calibration systems to be compared fairly to traditional output errors (e.g.,howwell does simulation output match utility bills?) as well as input-side errors (e.g., how well, variableby- variable, did the calibration capture the true building's description?). This system is then used to generate data for a correlation study of output and input error measures that validates CV(RMSE) and NMBE metrics put forth by ASHRAE Guideline 14 and suggests possible alternatives. [ABSTRACT FROM AUTHOR]

Outdoor airflow rate, building static pressure, supply air duct static pressure, and relief air plenum static pressure, as controlled variables, are maintained by modulating the speed of the supply and return fans and the position of the outdoor, recirculating, and relief air dampers in an air-handling unit (AHU). The three dampers are often interlinked completely or partially to match independent control inputs with the controlled variables. The traditional damper control has all three dampers interlinked with no control over the relief air plenum static pressure. Reverse relief airflow might occur as the outdoor air damper approaches the closed position. To prevent reverse airflow, one solution is to decouple the relief air damper and maintain positive static pressure at the relief air plenum. Two control methods are available based on the controlloopdesign. The first controlmethoduses the return fan speed to control the relief air plenum static pressure and the relief airdamperto control the building static pressure, and the second control method switches two control inputs. The purpose of this paper is to evaluate the controllability of the reverse relief airflow and building static pressure of these two control methods. The system performance is investigated through steady-state simulations on an AHU at different outdoor airflows using a nonlinear network solution. The simulation results show that both the control methods can well prevent reversere lief air flow. However, the first control method with a constant pressure setpoint may result in negative building static pressure at lower outdoor airflow ratio and either excessivelypositivebuildingstaticpressureorexcessivereturn fan power at higher outdoor airflow ratio. The second control method shows better control over the building static pressure control. A reset pressure scheme should be associated with the first control method when the relief damper is fully open. [ABSTRACT FROM AUTHOR]

Air leakage data from six parallel fan-powered terminal units (FPTUs) that used electronically commutated motors were evaluated to determine if simple models of air leakage could be developed for applications in building energy simulation programs. The data were from an earlier investigation by Edmondson et al. (2011). Units with both 8 and 12 in.(20.3 and 30.5 cm) primary inlets from three manufacturers were evaluated. The analysis included the impact of downstream static pressure, upstream static pressure, and primary airflow on the leakage from the units. Data for downstream static pressures ranged from approximately 0.1 to 0.5 in. w.g. (25 Pa to 125 Pa) and ranged from approximately 0.4 to 2.0 in. w.g. (100 Pa to 498 Pa) for upstream static pressures. Because the original data included some primary airflows outside the expected range of operation of the 8 and 12 in. (20.3 and 30.5 cm) FPTUs, the data set was reduced to include only data in the expected primary airflow operating ranges of these sized units. All the original leakage data were collected with theFPTUfan off and with only primary air flowing through the unit. No attempt was made to quantify how much leakage was coming through differing sources of leakage, such as the seams, penetrations, or backdraft damper. Leakage was found to be primarily dependent on downstream static pressure. A simple regression of leakage and downstream static pressure yielded correlations with Rsquared values from 0.80 to 0.96. Leakage airflow showed little variation with either upstream static pressure or primary airflow when the downstream static pressure was held constant. Three leakage classifications were identified--low, medium, and high--based on the measured data. Correlations that describe the leakage as a function of downstream static pressure for each classification were developed. Calculations for a system with downstream static pressures varying with the square of primary airflow were performed and results presented. These leakage correlations can be used in building simulation models that use mass and energy balance approaches to modeling FPTUs. [ABSTRACT FROM AUTHOR]