Sensitivity analysis for building energy simulation model calibration via automatic differentiation

Sisi Li, Mengchu Zhou, Kun Deng, Zhen Song, Yan Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

High-fidelity building energy simulation models are established on well-known physical laws and attempt to faithfully represent a real building. The numerical solution of differential algebraic equations that interpret thermal balance of buildings is included in building energy simulation models. It thereby leads to highly discontinuous systems that involve a large number of sub-routine calls and model switches during the execution. To acquire a building energy simulation model with good quality, parameter sensitivity analysis is well-advocated since it aims to target those parameters from the parameter pool in a specific building that hold more influence on the building thermal performance than others. Since building energy simulation models are given in a large piece of program codes and encapsulate a series of sub-models, the existing sensitivity analysis is built on Monte Carlo simulation and statistics-based random sampling methods only, e.g., Monte Carlo sampling and Latin Hypercube sampling methods, which are extremely time-consuming. We propose to perform the sensitivity analysis of a first-principle high-fidelity building energy simulation model via a straightforward differential sensitivity analysis method that relies on the estimation of derivatives. A key technical challenge is that the complexity of the model prohibits the analytical differentiation, while the numerical differentiation is sensitive to step size and suffers from the truncation error. We, hence, propose to adopt an automatic differentiation method, which exploits the operator overload feature of object oriented programming language, to obtain accurate numerical estimations of derivatives in an automated and computationally efficient way.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
PublisherIEEE Computer Society
Pages607-612
Number of pages6
ISBN (Print)9781479931064
DOIs
StatePublished - Jan 1 2014
Event11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 - Miami, FL, United States
Duration: Apr 7 2014Apr 9 2014

Publication series

NameProceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

Other

Other11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
Country/TerritoryUnited States
CityMiami, FL
Period4/7/144/9/14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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