Annual performance analysis of heat emission in radiator and underfloor heating systems in the European reference room.

Võsa, Karl-Villem; Ferrantelli, Andrea; Kurnitski, Jarek

doi:10.1051/e3sconf/201911104009

Cited by 4 publications

(9 citation statements)

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“…The optimal parameter combinations resulted in an annual heating energy reduction of up to 9% or 7 kWh/m 2 /year. The comparison of heat emitters and controllers in the European standard room shows similar results with 5% to 10% savings for the PI controlled UFH compared with the on-off control [20]. This does not compare to the 32% achieved for radiators in [23], however, the actual difference is difficult to compare as the baselines are different.…”

Section: Discussionmentioning

confidence: 90%

“…This means that setting PI parameters manually by trial and error, which is the common practice for PI tuning, would take a lot of time. For self-tuning controllers, simple tests would be needed but also these can be too time-consuming.When the gains are optimized, PID control can save energy in UFH control compared with the standard on-off control [20]. However, the optimal parameter values are usually not revealed.…”

mentioning

confidence: 99%

“…With quality tuning, PID could both reduce wasting energy and ensure comfort. The parameter optimization for UFH has been performed in extensive simulations [20], but it remains unknown whether it is possible to obtain the optimal parameters with shorter tests.Therefore, the aim of this work is two-fold. Firstly, to determine how UFH control can be improved by the application of PI parameters specifically derived for underfloor heating in nZEB with various tests and methods.…”

mentioning

confidence: 99%

“…When the gains are optimized, PID control can save energy in UFH control compared with the standard on-off control [20]. However, the optimal parameter values are usually not revealed.…”

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confidence: 99%

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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: Discussionmentioning

confidence: 90%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…When the gains are optimized, PID control can save energy in UFH control compared with the standard on-off control [20]. However, the optimal parameter values are usually not revealed.…”

mentioning

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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A novel method for calculating heat emitter and controller configuration setpoint variations with EN15316-2

Võsa

Ferrantelli

Kurnitski

2020

Journal of Building Engineering

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Estimating heat emission losses of heating systems is an important task of energy efficiency assessments in buildings. To this aim, the present international standards contain tabulated values for different emitter and control system configurations, without however explaining how to compute the effect of increased setpoint temperatures on the system losses. Moreover, the effects of each component are treated as independent, while e.g. vertical stratification and temperature control of the system are cross-related.In this paper we attempt to fill this gap by proposing a calculation method to calculate the product category specific setpoint variations for space heating emitters, accounting for the overall heat balance in the enclosure and including the cross-correlations of each component as well.The emission losses of a heating system are computed using a temperature setpoint variation method that is imposed on annual energy calculations. This complements the procedure presented in the Standard EN15316-2, also providing the possibility to use product-specific values of setpoint variations instead of tabulated values. As the main finding of the study, the calculation process is defined for a European Reference Room, namely for a specific enclosure that allows an accurate and transparent evaluation of the total setpoint variation.The product-specific values of setpoint variations are calculated from measured vertical stratification and control parameters with an annual simulation model of the European Reference Room. The total setpoint variations were simulated for a set of heat emitters and controllers in order to quantify and compare the energy performance of a new and an old type building located in Strasbourg. We find that the total setpoint variation required to overcome emission losses is up to 2.00 °C in the old building and 1.20 °C in the new building, corresponding respectively to an increase in total heating energy usage of up to 22% and 20%.

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Energy saving potential with smart thermostats in low-energy homes in cold climate

et al. 2020

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Smart home systems with smart thermostats have been used for years. Although initially mostly installed for improving comfort, their energy saving potential has become a renowned topic. The main potential lies in temperature reduction during the times people are not home, which can be detected by positioning their phones. Even if the locating is precise, the maximum time people are away from home is short in comparison to the buildings’ time constants. The gaps are shortened by the smart thermostats, which start to heat up hours before occupancy to ensure comfort temperatures at arrival, and low losses through high insulation and heat-recovery ventilation in new buildings, which slow down the cool-down process additional to the thermal mass. Therefore, it is not clear how high the actual savings can be for smart thermostats in new buildings. In this work, a smart radiator valve was installed for a radiator in a test building. Temperature setback measurements were used to calibrate a simulation model in IDA ICE. A simulation analysis was carried out for estimating the energy saving potential in a cold climate for different usage profiles.

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Annual performance analysis of heat emission in radiator and underfloor heating systems in the European reference room.

Cited by 4 publications

References 10 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

A novel method for calculating heat emitter and controller configuration setpoint variations with EN15316-2

Energy saving potential with smart thermostats in low-energy homes in cold climate

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