Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning

Fiducioso, Marcello; Curi, Sebastian; Schumacher, B.; Gwerder, Markus; Krause, Andreas

doi:10.24963/ijcai.2019/811

Cited by 43 publications

(25 citation statements)

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“…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. The reduction of 7 kWh/m 2 /year here can be seen as highly significant as this can be achieved with only parameter correction, which does not require intensive computation when the simple tests are applied.…”

Section: Discussionmentioning

confidence: 91%

“…However, the effects of PI parameters have been analyzed for radiators, as the heating circuits are, in reality, often not tuned and there is a lot of potential for energy saving [22]. Tuning radiator PID parameters with machine learning has shown a 32% reduction in heating energy consumption compared with Ziegler-Nichols tuning [23]. The current situation shows that while PID and on-off control waste energy, more advanced solutions on the market do not ensure comfort [24].…”

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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: 91%

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confidence: 99%

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

2020

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“…The constraints (5b) are enforced for all possible uncertainty values δ ∈ ∆ in which the uncertainty set ∆ is, in most situations, a continuous set containing an infinite number of instances -resulting in (5) being a semi-infinite optimization problem that is difficult to solve, especially when F (•, δ) or G(•, δ) are non-convex for any δ ∈ ∆. Note that no additional assumptions are imposed on Θ, which can have a mixture of continuous, discrete, and categorical components.…”

Section: Problem Formulationmentioning

confidence: 99%

“…As such, BO is useful whenever the objective function is expensive to evaluate, one does not have access to derivative information, and when the objective is nonconvex with many local optima [8]- [10]. Recently, BO has been applied to tuning of model predictive control (MPC) [4], [6] and other control architectures [5], [7]. Although promising results observed in practice, these works lack formal guarantees on the solution quality.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees

Paulson

Mesbah

2021

IEEE Control Syst. Lett.

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Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of blackbox optimization methods for automated controller tuning, they generally lack formal guarantees on the solution quality, which is especially important in the control of safetycritical systems. This paper focuses on obtaining closed-loop performance guarantees for automated controller tuning, which can be formulated as a black-box optimization problem under uncertainty. We use recent advances in non-convex scenario theory to provide a distribution-free bound on the probability of the closed-loop performance measures. To mitigate the computational complexity of the data-driven scenario optimization method, we restrict ourselves to a discrete set of candidate tuning parameters. We propose to generate these candidates using constrained Bayesian optimization run multiple times from different random seed points. We apply the proposed method for tuning an economic nonlinear model predictive controller for a semibatch reactor modeled by seven highly nonlinear differential equations.

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“…In [3], for example, the temperature and humidity of an individual room are controlled by means of a PID controller sending control signals to the supply air flap and the humidifier of a ventilation device. In [4], the room temperature is also maintained using a PID controller, whose parameters are determined by means of Bayesian optimization [5] without user intervention. Furthermore, in [6], a PID controller for regulating room temperature with an air conditioning system is implemented by a microcontroller.…”

Section: Related Workmentioning

confidence: 99%

Provision of Data to Use in Artificial Intelligence Algorithms for Single Room Heating

2021

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This paper describes a way to generate a great amount of data and to use it to find a relation between a room controller and a certain room. Therefore, simulation scenarios are defined and developed that contain different room, location, usage and controller models. With parameter variation and optimization of the corresponding controller parameters a data basis is created with about 5300 entries. On the basis of this data, machine learning algorithms like artificial neural networks can be used to investigate the relation between rooms and their best suited controllers.

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Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning

Cited by 43 publications

References 2 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

Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees

Provision of Data to Use in Artificial Intelligence Algorithms for Single Room Heating

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