PI Parameter Influence on Underfloor Heating Energy Consumption and Setpoint Tracking in nZEBs

Tuule Mall Kull, Martin Thalfeldt and Jarek Kurnitski
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Tuule Mall Kull: Nearly Zero Energy Buildings Research Group, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Martin Thalfeldt: Nearly Zero Energy Buildings Research Group, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Jarek Kurnitski: Nearly Zero Energy Buildings Research Group, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia

Energies, 2020, vol. 13, issue 8, 1-20

Abstract: In rooms with underfloor heating (UFH), local on–off controllers most often regulate the air temperature with poor accuracy and energy penalties. It is known that proportional–integral (PI) controllers can regulate most processes more precisely. However, hydronic UFH systems have long time constants, especially in low-energy buildings, and PI parameters are not easy to set manually. In this work, several potential PI parameter estimation methods were applied, including optimizing the parameters in GenOpt, calculating the parameters based on simplified models, and tuning the parameters automatically in Matlab. For all found parameter combinations, the energy consumption and control precision were evaluated. Simpler methods were compared to the optimal solutions to find similar parameters. Compared with an on–off controller with a 0.5 K dead-band, the best PI parameter combination found was with a proportional gain of 18 and an integration time of 2300 s, which could decrease the energy consumption for heating by 9% and by 5% compared with default PI parameters. Moreover, while GenOpt was the best method to find the optimal parameters, it was also possible with a simple automatic test and calculation within a weekend. The test can be, for example, 6-h setbacks applied during the nights or weekend-long pseudo-random changes in the setpoint signal. The parameters can be calculated based on the simplified model from these tests using any well-known simple method. Results revealed that the UFH PI controller with the correct parameters started to work in a predictive fashion and the resulting room temperature curves were practically ideal.

Keywords: IDA ICE; building simulation; intermittent heating; model predictive control (MPC); heat pumps; proportional–integral–derivative (PID) control; thermostats (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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