Sensitivity Analysis for Building Energy Simulation Model Calibration via Algorithmic Differentiation

Document Type

Article

Publication Date

4-1-2017

Abstract

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.

Identifier

84978288354 (Scopus)

Publication Title

IEEE Transactions on Automation Science and Engineering

External Full Text Location

https://doi.org/10.1109/TASE.2016.2573821

ISSN

15455955

First Page

905

Last Page

914

Issue

2

Volume

14

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