Building Information Modeling (BIM) And Building Energy Modeling (BEM): Assessment of BIM-BEM Workflows And Energy Simulation Tools

Interoperability and Integration of the Building Information Modeling (BIM) and Building Energy Modeling (BEM) tools are major challenges for the Architecture, Engineering, and Construction (AEC) industry. The goals of this dissertation were to (i) investigate various BEM tools to evaluate their workflow and integration with BIM and (ii) assess BEM tools’ accuracy in predicting/simulating whole building energy performance from a broader lens to a more specific one (i.e., EUI to end-use data, respectively). To conduct the research study, case study buildings were selected from various categories, representing different building types (i.e., academic, administrative, and recreational). The case study buildings were located on the campus of the University of Massachusetts Amherst (UMass Amherst), and the main criterion in their selection was the accessibility of their construction document and measured energy data. BEM tools from three categories of BIM-integrated, BIM-interoperable, and BIM-separated BEM tools were selected, aiming to evaluate design-analysis workflows. To comparatively analyze the simulated energy data against each other and measured data, case study buildings’ actual energy data was collected and used. The research findings suggest potential developments that are essential to accomplish a streamlined BIM to BEM workflow, making it less tedious and time-consuming

Integration of Building Energy Modeling (BEM) and Building Information Modeling (BIM): Workflows and Case Study

Building Energy Modeling (BEM) intends to quantify buildings’ energy performance to help designers and architects better understand the environmental impacts of their decisions. Building Information Modeling (BIM) refers to a digital, model-based representation, where information about building design can be shared among different stakeholders and used during all stages of buildings’ lifecycle. The purpose of this research was to investigate integration of BEM and BIM, using two analysis tools. Green Building Studio (GBS) and Sefaira are two performance analysis software programs, which can be used both in the form of BIM plug-in/built-in tools, as well as web applications to analyze and quantify energy performance of buildings. To capture their level of integration with BIM, an existing Campus Recreation Building on UMass Amherst campus was used as a case study to evaluate modeling processes, requirements, and workflows. Comparative analysis between modeled and actual energy consumption data was also performed to analyze accuracy of the different simulation programs. This paper discusses each tool capabilities and drawbacks in providing accurate energy analysis procedures and results

Additive vs. Area-Weighted Thermal Resistance in Building Facades: Assessment of Thermal Bridging Effects on Buildings’ Energy Performance

The most common approach for calculating thermal resistance (R-value) of building facades is based on the additive method, where material components of the facade in sectional view, their relative thickness and thermal conductivity are considered. However, in order to account for thermal bridging caused by framing, area-weighted approach should be used to determine more accurate R-value. This approach also considers plan view of building facade, and the properties of framing components. The main objective of this research was to investigate the effects of facades’ thermal resistance (additive vs. area-weighted R-values) on buildings’ energy performance.Research methods included data collection, modeling, simulations and comparative analysis of results. An existing Campus Recreation Building, located at the University of Massachusetts Amherst, was used as a case study building. First, the original construction documentation was reviewed to create a 3D model in Revit. Facade material components and specifications were used to determine properties of the opaque facade system, consisting of a brick cavity wall with steel stud framing. R-values for this facade system were calculated using additive and area-weighted methods. Then, a building energy analysis simulation program Green Building Studio was used for analysis, where one energy model was created to analyze the impacts of two different R-values on the overall energy consumption of this building. Other inputs, such as building geometry, occupancy schedules, glazing materials, etc. were identical in both models. Energy modeling results were compared to actual energy consumption data, collected over a period of one year. Simulation results showed that energy consumption was 2.5% higher when area-weighted R-value was applied

Effect of Thermal Bridging on Buildings\u27 Energy Performance: Comparison of Area-Weighted vs. Additive Thermal Resistance in Facades

Heat transfer through building facades can occur by any combinations of conduction, convection, and/or radiation. Conductive heat transfer depends on materials’ thermal conductivity (λ) and thickness (d), which influence building envelope’s thermal resistance (R-value). The most common approach for calculating R-value of building facades is based on the additive method, where material components of the facade in sectional view, their relative thickness and thermal conductivity are considered. However, in order to account for thermal bridging caused by framing, area-weighted approach should be used to determine more accurate R-value. This approach also considers plan view of building facade, and the properties of framing components. The main objective of this research was to investigate the effects of facades’ thermal resistance (additive vs. area-weighted R-values) on buildings’ energy performance. Research methods included data collection, modeling, simulations and comparative analysis of results. An existing Campus Recreation Building on UMASS Amherst campus was used as a case study building. First, the original construction documentation was reviewed to create a 3D model in Revit. Facade material components and specifications were used to determine properties of the opaque facade system, consisting of brick cavity wall with steel stud framing. R-values for this facade system were calculated using additive and area-weighted methods. Then, a building energy analysis simulation program Green Building Studio was used to calculate annual and monthly energy consumption for the case study building, where one energy model was created to analyze the impacts of two different R-values on the overall energy consumption of this building. Other inputs, such as building geometry, occupancy schedules, glazing materials, etc. were identical in both simulation scenarios. Energy modeling results were compared to actual energy consumption data, collected over a period of one year. Simulation results showed that energy consumption, cost, energy usage intensity, carbon emission, and heating loads were higher with area-weighted method, and lower with additive approach