Scenario-based model predictive control approach for heating systems in an office building

Scenario-based model predictive control approach for heating systems in an office building

Pippia, T.
Lago, J.
Coninck, R.D.
Sijs, J.
Schutter, B.D.

2019

In the context of building heating systems control in office buildings, the current state-of-the-art applies either a deterministic Model Predictive Control (MPC) controller together with a nonlinear model, or a linearized model with a stochastic MPC controller. Deterministic MPC considers only one realization of the external disturbances, which can lead to a low performance solution if the forecasts of the disturbances are not accurate. Similarly, linear models are simplified representations of the building dynamics and might fail to capture some relevant behavior. In this paper, we improve upon the current literature by combining these two approaches, i.e. we adopt a nonlinear model together with a stochastic MPC controller. We consider a scenario-based MPC (SBMPC), where many realizations of the disturbances are considered, so as to include more possible future trajectories for the external disturbances. The adopted scenario generation method provides statistically significant scenarios, whereas so far in the current literature only approximate methods have been applied. Moreover, we use Modelica to obtain the model description, which allows to have a more accurate and nonlinear model. Lastly, we perform simulations comparing standard MPC vs SBMPC vs an optimal control approach with measurements of the external disturbances, and we show how our proposed scenario-based MPC controller can achieve a better performance compared to standard deterministic MPC. © 2019 IEEE.

Building automation
Building heating systems
Model predictive control
Scenario-based control
Controllers
Heating equipment
Intelligent buildings
Nonlinear systems
Office buildings
Predictive control systems
Stochastic models
Stochastic systems
Structural dynamics
Approximate methods
Building heating
Deterministic modeling
External disturbances
Scenario generation
Scenario-based modeling
Model predictive control

http://resolver.tudelft.nl/uuid:fc4b9787-76ee-490b-8dd8-0a7be7da561e

869619

IEEE Computer Society

9781728103556

2161-8070

IEEE International Conference on Automation Science and Engineering, 15th IEEE International Conference on Automation Science and Engineering, CASE 2019, 22 August 2019 through 26 August 2019, 1243-1248

8842846

conference paper