Identification of aggregate building thermal dynamic model and unmeasured internal heat load from data

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper is on the problem of simultaneously identifying the parameters of an aggregate thermal dynamic model of a multi-zone building and unknown disturbances from input-output data. 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 that delivers thermal energy to the entire building. A key challenge in identification is the presence of unknown disturbance since it is not measurable but non-negligible.We first present a principled method to aggregate a multizone building model into a single zone model. We then provide a method to identify thermal parameters and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data generated from a virtual building. 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.

Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2958-2963
Number of pages6
ISBN (Electronic)9781728113982
DOIs
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
Country/TerritoryFrance
CityNice
Period12/11/1912/13/19

Funding

ZG, AC, and PB are with the Department of Mechanical and Aerospace Engineering at the University of Florida. PI and JM are with the Building Envelope & Urban Systems Research group at Oak Ridge National Laboratory, Oak Ridge, Tennessee. The research reported here has been partially supported by the NSF through awards 1463316 (CMMI) and 1646229 (CPS), 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 Foundation1463316, 1646229

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