Sustainability is important in the building sector because of the substantial impacts to the environment, economy, and the occupants. The United Nations Environment Programme estimates that buildings alone account for 30% of global raw material use, 25% of solid waste generation, 25% of water use, 12% of land use, and 33% of greenhouse gas (GHG) emissions. Operational energy consumption of buildings account for approximately 70% of the lifecycle environmental effects of these systems overall, though there is wide variation among individual buildings. Economically, buildings require significant financial resources to design, construct, and operate. Socially, the built environment influences human well-being as people spend most their time indoors. Consequently, assessing building design strategies from triple-bottom line perspective is paramount to achieving overarching sustainability goals.
The window to wall ratio (WWR), or the proportion of glazing in a building’s envelope, influences several design and performance features of a building across its life cycle, including material use, daylight autonomy, useful daylight illuminance, solar gain and effects on HVAC operation and sizing, and lighting energy consumption. Furthermore, reducing the WWR has also been studied as a resilient building strategy, with a goal of protecting the façade by increasing elasticity during relative displacements (earthquakes) and preventing air and water intrusion. The objective of this study is to assess the environmental, economic, and social effects of various WWR using both simulated data and statistical analyses of surveyed building performance data.
A building information model of the US DOE’s large office (12 story) prototype building located in Boston, MA was developed using Autodesk Revit®. The Tally® Revit application and DOE’s EnergyPlusTM were used to perform the LCA and assess implications on HVAC equipment sizing, embodied energy of materials, and distribution of operational energy. The simulated energy results are compared with a statistical regression model of building energy consumption utilizing the US Energy Information Administration’s (EIA) Commercial Building Energy Consumption Survey (CBECS). The regression is trained with window assembly features and WWR, among other design criteria, as predictor variables. The statistical model will be used to assess confidence in the simulated results. Designs altering the window to wall ratio (WWR) from the baseline 40% were compared using lifecycle sustainability assessment (LCSA), considering environmental, costing, and occupant comfort as performance metrics.
Preliminary simulated results of reducing the WWR to 20% show reduced primary energy demands and reductions in the majority of LCA impact categories. Early diagnostics of the regression model also support the simulated energy results. Lifecycle costing will be performed to assess tradeoffs in upfront and operating costs. Daylight autonomy and occupant comfort will be assessed using ASHRAE Standard 55 – Thermal Environmental Conditions for Human Occupancy.
• Complexity, resilience and sustainability , • Sustainability and resilience metrics , • Infrastructure systems, the built environment, and smart and connected infrastructure