Sensitivity Analysis for Building Energy Simulation Model Calibration via Algorithmic Differentiation

High-fidelity building energy simulation models are software tools that are built on the well-established physical laws of thermal/hygric processes to model heating, cooling, lighting, ventilation, and energy use of buildings. They are highly nonlinear systems that involve a large number of subroutine calls and submodel switches during execution. To calibrate a building energy simulation model with good quality, parameter sensitivity analysis is well advocated, since it aims to identify those parameters in a specific building that hold more influence on the building thermal performance than others to facilitate shortening the lengthy cycle of model calibration procedures. Since simulation models are given in a large piece of source codes and encapsulate a series of submodels, the prevailing sensitivity analysis is mostly built within the framework of Monte Carlo simulation and statistics-based random sampling methods. It is computationally intensive. We propose to perform such analysis via a differential sensitivity analysis method that relies on the estimation of derivatives. A key technical challenge is that the high nonlinearity of the model prohibits any analytical differentiation, while numerical differentiation is too sensitive to step size and suffers from a truncation error. We, hence, propose to adopt an algorithmic differentiation method, which exploits the operator overload feature of object-oriented programs, to obtain accurate and robust numerical estimations of derivatives in an automated and computationally efficient way.