Modelica based Design and Optimisation of Control Systems  for Solar Heat Systems and Low Energy Buildings

Seidel, Stephan; Clauß, Christoph; Haufe, Jürgen; Majetta, Kristin; Blochwitz, Torsten; Liebold, Edgar; Hintzen, Ullrich; Klostermann, Volker

doi:10.3384/ecp15118401

Cited by 2 publications

(5 citation statements)

References 6 publications

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“…The process gain (K p ) has two clearly different orders and altogether three different levels. The values were around 1 for all cases where the PRBS signal was used as the setpoint and were much larger for Energies 2020, 13,2068 8 of 20 other cases. For the ideal step and measured setbacks, the K p value was around 20, for all other setback cases, around 40.…”

Section: Found Simplified Modelsmentioning

confidence: 91%

“…The models were used to either autotune the parameters in Matlab or to calculate the parameters using well-known methods such as AMIGO, SIMC, and Cohen-Coon. Both Energies 2020, 13,2068 4 of 20 of these approaches are also clarified in Section 2.5. The performance of all the gained parameters was cross-checked in both rooms over the whole heating period.…”

Section: Outline Of the Workmentioning

confidence: 99%

“…Energies 2020, 13 The hilimit and lolimit are the limits for the PI output signal. In this work, the PI output signal is the fraction of the nominal mass flow to the UFH and is, therefore, limited from 0 to 1.…”

Section: Amentioning

confidence: 99%

“…Also, building blocks have been developed to be compatible with detailed modelling so that engineers can do the tuning. However, the process is still not fully automated [13]. Auto-tuning of PID controllers for heating, cooling, and ventilation plants have been described both several decades ago [14-16] and more recently [17].…”

mentioning

confidence: 99%

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

2020

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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.Energies 2020, 13, 2068 2 of 20 the input signal is, for example, the electricity price or other signal from the grid and the output of the optimization are the air temperature setpoints of the rooms [3].The setpoints in rooms have to be tracked by room-based controllers, such as thermostats or proportional-integral-derivative (PID) controllers, as the supervisory control can deal with optimization but not with the local fast changes [8]. PID is commonly known to be one of the best and easiest feedback controllers for any process. For buildings, as a relatively slow system, the derivative part is usually dropped and PI controllers are used instead. However, choosing improper PID gains (parameters) could result in making the whole system unstable. Therefore, designers and researchers often turn to optimal or predictive solutions [9].However, advanced solutions are not easy to implement and the need for robust and reliable solutions with minimal human interaction is evident. [10,11]. To simplify or avoid technicians' inputs to the systems, the control algorithms can be tested in realistic environments [12]. Also, building blocks have been developed to be compatible with detailed modelling so that engineers can do the tuning. However, the process is still not fully automated [13]. Auto-tuning of PID controllers for heating, cooling, and ventilation plants have been described both several ...

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Section: Found Simplified Modelsmentioning

confidence: 91%

Section: Outline Of the Workmentioning

confidence: 99%

Section: Amentioning

confidence: 99%

mentioning

confidence: 99%

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

2020

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“…While the undercooling problem may be tackled by appropriate shift of temperature setpoints (e.g. [11]), the overheating problem can be best predicted via weather forecast. In the following, a supply temperature control approach via Q-learning is proposed to solve both comfort problems.…”

Section: Comfort Problemmentioning

confidence: 99%

Cascaded reinforcement learning based supply temperature control

Huang,

Seidel,

Bräunig

2023

J. Phys.: Conf. Ser.

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In this work, a Q-learning based supply temperature control approach for a demonstrator building is proposed. The purpose is to improve the temperature behaviour inside the building and to tackle comfort problems such as overheating and undercooling which cannot be coped with by the standard heating curve. The Q-learning controller considers predicted future weather data as system states. This can be shown to be superior to Q-learning controllers without weather prediction. Furthermore, in order for the controller to capture different thermal effects of different time constants, a cascaded control structure is designed: An inner Q-learning based controller deals with thermal effects of smaller time constants. It is wrapped by an outer slower Q-learning controller which can tackle effects of larger time constants. Therewith, further improvement of comfort can be achieved.

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Modelica based Design and Optimisation of Control Systems for Solar Heat Systems and Low Energy Buildings

Cited by 2 publications

References 6 publications

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

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

Cascaded reinforcement learning based supply temperature control

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