Energy Performance Prediction Model for Hurricane-Damaged Building Envelopes

Provides Building Energy Performance Access by Connecting Structural Engineering and Energy Efficiency in Five Interconnected Analyses

This five-stage analysis system predicts energy consumption in buildings following a natural hazard based on wind-induced damage to their building envelopes from the hazard. Buildings account for approximately 40% of total energy consumption and 30% of global greenhouse gas emissions. Research emphasizes global sustainability efforts to reduce building energy demand, with initiatives focusing on implementing energy-efficient systems and technologies. However, the effects of external hazards, such as hurricanes, on building energy consumption remain an overlooked subject. Building envelopes serve as a building’s protective barrier against environmental conditions, playing a key role in determining its energy demand. Once building envelope components are damaged, energy use can increase significantly. Additionally, current building energy simulation tools lack the capability to predict the change in building energy consumption as the result of wind-induced building envelope damage. Hence, there is a need for a novel method to assess the effects of wind-induced damage on a building’s post-hazard energy performance.

Researchers at the University of Florida have developed a model to accurately predict energy consumption in hurricane-damaged buildings. Through five interconnected analyses, the model estimates post-hazard energy use to support precise cost projections for envelope enhancements to improve resilience.

Application

Five-stage analysis system predicts energy consumption in hurricane-damaged buildings

Advantages

• Links hurricane-driven building damage to long-term energy efficiency, enabling more sustainable design choices and retrofit strategies that reduce operating costs and environmental impacts
• Incorporates Monte Carlo uncertainty analysis of extreme wind events, providing insights into mitigation strategies and policymaking
• Quantifies the impact of hurricane damage on building envelopes, supporting retrofit strategies that reduce energy consumption following a natural hazard
• Updates dynamically to track the number of damaged building envelope components, allowing for more precise cost projections for envelope enhancements and resilience improvements
• Performs iterative damage and post-hazard aerodynamic analyses, enabling more accurate energy consumption estimation in damaged buildings
• Functions as an integrated assessment model, aiding in the selection and optimization of facade systems based on site-specific conditions and energy performance objectives
• Utilizes site-specific data, facilitating implementation for any building and location
• Utilizes knowledge from a variety of disciplinary fields by incorporating results from physical experiments, empirical modeling methods, and the practical guidance of engineering standards and codes

Technology

This prediction model evaluates the effects of wind-induced hurricane damage on a building envelope’s energy performance in five interconnected analyses. The first stage is a hazard analysis to anticipate wind intensity using site-specific data. Then, an aerodynamic analysis assesses how wind interacts with the building. Next, the model performs a structural analysis to determine the building’s structural integrity based on how it responds to wind loads, especially during extreme wind events. Damage analysis then evaluates the damage consequences that impact the building’s energy performance. Based on the wind-induced damage, post-hazard aerodynamic analysis analyzes airflow within the building due to openings in the building envelope. Lastly, energy analysis integrates the results with building energy modeling to estimate post-hazard energy consumption. The model updates dynamically, continuously assessing damaged building envelope components and openings for accurate predictions. To account for variability and uncertainty in extreme wind events, the model also performs Monte Carlo uncertainty analysis.