Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance

Zhong Guo, Austin R. Coffman, Jeffrey Munk, Piljae Im, Teja Kuruganti, Prabir Barooah

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem of simultaneously identifying an aggregate thermal dynamic model and unknown disturbances from input–output data of multi-zone buildings. The unknown disturbance is a key challenge since it is not measurable but non-negligible. We first present a principled method to aggregate a multi-zone building model into a single zone model, and show the aggregation is not as trivial as it has been assumed in the prior art. We then provide a method to identify the parameters of the model and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data collected from a multi-zone building testbed in Oak Ridge National Laboratory. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain, even in the case of a specially designed test in which the disturbances affecting each zone are known (as the case of our experimental testbed). This insight is used to provide a heuristic that can be used to assess when the identification results are likely to have high or low accuracy.

Original languageEnglish
Article number110500
JournalEnergy and Buildings
Volume231
DOIs
StatePublished - Jan 15 2021

Funding

The research reported here has been partially supported by the NSF through awards 1463316, 1646229, 1934322, and DOE GMLC grant titled “virtual batteries”. The authors would like to thank Naren Raman and Bo Chen for help with the CasADi software.

FundersFunder number
DOE GMLC
National Science Foundation1934322, 1646229
Directorate for Engineering1463316

    Keywords

    • Building thermal dynamics modeling
    • Data-driven modeling
    • Disturbance estimation
    • System identification

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