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

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 mo...

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Veröffentlicht in:Energy and buildings 2021-01, Vol.231, p.110500, Article 110500
Hauptverfasser: Guo, Zhong, Coffman, Austin R., Munk, Jeffrey, Im, Piljae, Kuruganti, Teja, Barooah, Prabir
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container_end_page
container_issue
container_start_page 110500
container_title Energy and buildings
container_volume 231
creator Guo, Zhong
Coffman, Austin R.
Munk, Jeffrey
Im, Piljae
Kuruganti, Teja
Barooah, Prabir
description 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.
doi_str_mv 10.1016/j.enbuild.2020.110500
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subjects Agglomeration
Air conditioning
Algorithms
Building thermal dynamics modeling
Buildings
Control equipment
Data-driven modeling
Disturbance
Disturbance estimation
Disturbances
Dynamic models
HVAC equipment
Identification methods
Model testing
Parameter identification
Research facilities
System identification
Test stands
Ventilation
title Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance
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