A case study for sensitivity-based building energy optimization
Abstract
Building design optimization process is associated with uncertainties due to
climate change, unpredictable occupant behavior, and physical degradation of building
material over time. The inherent uncertainties in the design process reduce the reliability and
robustness of the optimal design solution(s) and affect design decision-making results. This
research studies the capabilities of parametric design tools in adopting probabilistic methods
to handle uncertainties in building performance optimization. Variance-based methods, e.g.,
Monte Carlo sensitivity analyses are implemented to identify the most critical parameters in
design optimization problems and improve the efficiency of design optimization. The optimal
solutions achieved with variance-based methods are satisfying the design objectives more
efficiently, also remain robust to changes and uncertainties.
Subject
Building design optimizationVariance-based methods
Parametric design
Sensitivity analysis
Monte Carlo