Search

• Embodied and operational carbon tradeoffs in building envelopes are studied. • Envelope variables include wall assemblies, WWRs, glazing and shading. • Energy decarbonization models are used to determine the 30-year operational carbon. • Results show the importance of a total carbon approach to envelope design. • Over or under insulation can result in waste of 10–150 kgCO 2 e/m2. The building envelope has a substantial influence on a building's life cycle operational and embodied carbon emissions. Window-to-wall ratios, wall assemblies, shading and glazing types, have been shown to have a significant impact on total emissions. This paper provides building designers, owners, and policy makers with actionable guidance and a prioritization framework for establishing co-optimized lifecycle carbon performance of facade assembly components in a broad spectrum of climate contexts and energy carbon intensities. A large parametric study of building envelopes is conducted using building performance simulation and cradle-to-gate embodied carbon calculations in 6 US cities. The authors derive the total carbon emissions optimization for commercial office and residential space types using standard code-reference models and open-source lifecycle data. Comparisons between optimal total carbon solutions and (i) optimal operational carbon and (ii) minimum required assemblies, show the impact of under and over investing in envelope-related efficiency measures for each climate. Results show how the relationship between embodied and operational carbon is highly localized, that optimal design variables can vary significantly. In low carbon intensity energy grids, over investment in envelope embodied carbon can exceed as 10 kgCO 2 e/m2, while under investment in high carbon intensity grids can be higher than 150 kgCO 2 e/m2. [ABSTRACT FROM AUTHOR]

• A POE-integrated simulation method is proposed to address energy occupant behavior. • Opening windows and operating AC were found to co-exist which affects simulation accuracy. • The new method improves energy simulation accuracy by 14% for domestic areas. • The method improves energy simulation accuracy by 16% for total building areas. Occupant behavior plays a critical role in building energy consumption, particularly in residential buildings. However, occupant behavior is complicated and varies significantly from case to case. Also, energy modeling of high-rise buildings is far less explored than that of low- or medium-rise buildings. This paper aims to improve accuracy in building energy simulation by utilizing Post Occupancy Evaluation (POE) data to calibrate energy model. Drawing on a review of the literature of occupant behavior and building energy modeling, the paper provides a calibration method of integrating POE data into an energy model, which is demonstrated using a real-life typical 40-storey residential building in Hong Kong. The developed method addresses seven updated input parameters, namely, schedule, devices, air-conditioners, windows, lights, domestic hot water, and cooking. By comparing the two energy modeling processes, i.e. with and without POE input, and their resultant estimated energy consumption, the paper quantifies the impact of occupant behavior on building energy consumption. Annual metered data together with energy bills obtained from the POE were utilized to validate the models. The results show that the use of the developed POE-integrated method helped to improve accuracy in energy consumption prediction by 14% for the total building floor area and by 16% for the total residential area. From examining the impacts of the seven input parameters, the paper reveals the key energy use sensitive occupant behaviors within high-rise residential buildings in Hong Kong. These include: the number of residents in each unit; adoption of window type and split type of air-conditioners; window and air condition operation modes; cooking time on weekdays and weekends; and time spent on hot water showering. The developed method can assist building designers and services engineers to estimate building energy use more accurately and provides a scenario analysis tool for clients and facility managers to develop effective energy conservation strategies. [ABSTRACT FROM AUTHOR]

The objective of this study is to understand the role of life cycle analysis (LCA) in building system design process based on survey inputs from building system designers and a numerical sensitivity analysis. This paper first presents findings on the perceived importance of building life cycle assessment completed by 96 practicing designers from August 2012 to April 2013. The majority of respondents, approximately 70%, work as building system designers in the U.S. The building system designers are divided into three categories: (1) enclosure system designers, (2) mechanical system designers, and (3) designers working on both systems. One of the major survey findings is that life cycle assessments are much less used in building system design than energy simulations. The primary reason for performing a life cycle analysis for a building design project is a requirement from a building owner. Furthermore, Fisher's test shows that respondents’ profession, company size, and work experience have significant correlations with the deployment of energy simulations and life cycle assessments in building design projects. ANOVA-based analyses demonstrate that there is no statically significant difference among the three categories of system designer responses on the importance of building components and design selection criteria. Interestingly, the sensitivity analyses performed for the medium size DOE (Department of Energy) reference building indicate that wall assemblies have a much larger impact on building life cycle costs than window properties. The comparison between sensitivity analysis and survey results indicates that the influence of window properties on life cycle cost is over estimated by most of the surveyed participants. Overall, this study revealed that LCA is still not widely used in the building industry even though it would help address design biases toward building systems that do not deliver the expected performance. [ABSTRACT FROM AUTHOR]