The effect of uncertainty in whole building simulation models for purposes of generating control strategies

Amar Kumar Seeam, David Laurenson, Asif Usmani

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

Buildings consume a significant amount of energy worldwide in maintaining comfort for occupants. Building energy management systems (BEMS) are employed to ensure that the energy consumed is used efficiently. However these systems often do not adequately perform in minimising energy use. This is due to a number of reasons, including poor configuration or a lack of information such as being able to anticipate changes in weather conditions. We are now at the stage that building behaviour can be simulated, whereby simulation tools can be used to predict building conditions, and therefore enable buildings to use energy more efficiently, when integrated with BEMS. What is required though, is an accurate model of the building which can effectively represent the building processes, for building simulation. Building information modelling (BIM) is a relatively new method of representing building models, however there still remains the issue of data translation between a BIM and simulation model, which requires calibration with a measured set of data. If there a lack of information or a poor translation, a level of uncertaintly is introduced which can affect the simulation's ability to accurate predict control strategies for BEMS. This paper explores effects of uncertainty, by making assumptions on a building model due to a lack of information. It will be shown that building model calibration as a method of addressing uncertainty is no substitute for a well defined model.

Original languageEnglish
Title of host publicationIntelligent Environments 2021
Subtitle of host publicationWorkshop Proceedings of the 17th International Conference on Intelligent Environments
PublisherIOS Press
Pages45-55
Number of pages11
Volume29
ISBN (Electronic)9781643681870
ISBN (Print)9781643681863
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • Building model
  • Calibration
  • ESP-r
  • Uncertainty

ASJC Scopus subject areas

  • General Computer Science

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