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.
Regression Models for Output Prediction of Thermal Dynamics in Buildings
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.
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Chasparis, G. C., and Natschlaeger, T. (November 10, 2016). "Regression Models for Output Prediction of Thermal Dynamics in Buildings." ASME. J. Dyn. Sys., Meas., Control. February 2017; 139(2): 021006. https://doi.org/10.1115/1.4034746
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