Scenario-based model predictive control approach for heating systems in an office building
| Autor: | Bart De Schutter, Jesus Lago, Roel De Coninck, Joris Sijs, Tomas Pippia |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Office buildings
Computer science Stochastic modelling 020209 energy Heating equipment Deterministic modeling Building automation Context (language use) Astrophysics::Cosmology and Extragalactic Astrophysics 02 engineering and technology Modelica Predictive control systems Scenario-based control Intelligent buildings 020401 chemical engineering Control theory 0202 electrical engineering electronic engineering information engineering Nonlinear systems Model predictive control 0204 chemical engineering External disturbances Stochastic systems Controllers Approximate methods Linear model Building heating systems Scenario generation Optimal control Nonlinear system Stochastic models Structural dynamics Scenario-based modeling Building heating |
| Zdroj: | 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 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) CASE Proceedings of the 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE 2019) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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