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Application of Epistemic Uncertainty Analysis and Sensitivity Analysis in Green Construction Design

Building performance is often expressed according to primary energy use; however studies should also include environmental load. To mitigate the effects of increased CO2 emissions, green building designs are now increasingly popular options for stakeholders. In contrast, impacts of green design parameters, building performance assessment, and design optimization objectives are not addressed sufficiently for American infrastructure. Designing low-energy architecture to minimize carbon emissions requires thoughtful articulation of green building design alternatives. A common barrier for green building design in the U.S. is the time needed to identify and evaluate alternatives. Modeling energy consumption in buildings is essential for different applications, including building energy management and establishing baselines for sustainable building performance codes. This paper proposes a new optimization objective, which considers annual carbon emissions for multi-family construction in Florida. A multi-step sensitivity and epistemic uncertainty approach are proposed to identify the critical design parameters during the early stages of sustainable building design. Building simulation software was utilized to model green building configuration for carbon emission values, further analyzed using Monte Carlo and Morris Method sensitivity analysis techniques. The present study considered green building design alternatives as variables since carbon emission reduction potential is sensitive to these variables. Results indicate that PV panel efficiency, PV payback limit, lighting efficiency, plug load efficiency, and solar heat gain coefficient are highly influential parameters. The methodology is presented, and an example is applied to a new construction multi-family apartment design in Tallahassee, FL. This uncertainty and sensitivity analysis improved the design efficiency, while emphasizing usefulness in the green building optimization process.