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Research Papers

Regression Models for Output Prediction of Thermal Dynamics in Buildings

[+] Author and Article Information
Georgios C. Chasparis

Department of Data Analysis Systems,
Software Competence Center Hagenberg GmbH,
Softwarepark 21,
Hagenberg 4232, Austria
e-mail: georgios.chasparis@scch.at

Thomas Natschlaeger

Department of Data Analysis Systems,
Software Competence Center Hagenberg GmbH,
Softwarepark 21,
Hagenberg 4232, Austria
e-mail: thomas.natschlaeger@scch.at

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 2, 2015; final manuscript received August 22, 2016; published online November 10, 2016. Assoc. Editor: Umesh Vaidya.

J. Dyn. Sys., Meas., Control 139(2), 021006 (Nov 10, 2016) (9 pages) Paper No: DS-15-1476; doi: 10.1115/1.4034746 History: Received October 02, 2015; Revised August 22, 2016

Standard (black-box) regression models may not necessarily suffice for accurate identification and prediction of thermal dynamics in buildings. This is particularly apparent when either the flow rate or the inlet temperature of the thermal medium varies significantly with time. To this end, this paper analytically derives, using physical insight, and investigates linear regression models (LRMs) with nonlinear regressors (NRMs) for system identification and prediction of thermal dynamics in buildings. Comparison is performed with standard linear regression models with respect to both (a) identification error and (b) prediction performance within a model-predictive-control implementation for climate control in a residential building. The implementation is performed through the EnergyPlus building simulator and demonstrates that a careful consideration of the nonlinear effects may provide significant benefits with respect to the power consumption.

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References

Figures

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Fig. 1

Bond-graph approximation of heat-mass transfer dynamics of a thermal zone i∈I

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Fig. 2

System separation architecture under FI

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Fig. 3

One-step ahead prediction error of the zone temperature under the LRM and NRM prediction models

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Fig. 4

Comfort cost of the MPC (19) under the LRM and NRM prediction models

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Fig. 5

Heating and pump electricity cost of the MPC (19) under the LRM and NRM prediction models

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