From robust chip to smart building: CAD algorithms and methodologies for uncertainty analysis of building performance

Buildings consume about 40% of the total energy use in the U.S. and, hence, accurately modeling, analyzing and optimizing building energy is considered as an extremely important task today. Towards this goal, uncertainty/sensitivity analysis has been proposed to identify the critical physical and environmental parameters contributing to building energy consumption. In this paper, we propose to apply sparse regression techniques to uncertainty/sensitivity analysis of smart buildings. We consider the orthogonal matching pursuit (OMP) algorithm as a case study to demonstrate its superior efficacy over other conventional approaches. Experimental results reveal that OMP achieves up to 18.6× runtime speedups over the conventional least-squares fitting method without surrendering any accuracy.