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

Seeam, Amar ORCID: https://orcid.org/0000-0001-8203-1545, Laurenson, David and Usmani, Asif (2021) The effect of uncertainty in whole building simulation models for purposes of generating control strategies. In: Intelligent Environments 2021: Workshop Proceedings of the 17th International Conference on Intelligent Environments. Bashir, Engie and Luštrek, Mitja, eds. Ambient Intelligence and Smart Environments, 29 . IOS Press, pp. 45-55. ISBN 9781643681863, e-ISBN 9781643681870. [Book Section] (doi:10.3233/AISE210078)

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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.

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