Optimal selection of building components using sequential design via statistical surrogate models

Choosing the optimal combination of building components that minimize investment and operational costs is a topic of great importance in the building simulation community. Optimization using simulation tools, i.e., Energy Plus, becomes computationally expensive for traditional search approaches. An additional challenge is the complexity of the input parameter space, which is usually very large and contains both continuous and discrete variables. In this paper, we present a novel approach to address both of these problems. The key idea of the proposed approach is to first build a statistical surrogate model for the energy simulation model and to then update the surrogate model based on the concept of sequential design of experiments. We demonstrate the proposed approach using a case study of a live retrofit project for Building 661 at the Navy Yard of Philadelphia, USA. Results show that the statistical surrogate model allows for fast evaluation of the building's energy consumption, and the sequential design reduces the computational cost by requiring a smaller number of runs of the energy simulation model.