The performance of any model predictive control (MPC) strategy is in residential thermal energy management depends on the accuracy of the mathematical model describing the thermal loads and capacities, and on the quality of the forecasts of disturbances, such as weather and occupant behaviour. Deviating model parameters and inaccurate disturbance predictions are shown to give rise to increased energy costs and comfort violations. A distinction can be made between (1) parametric uncertainties (p), which means uncertainties that can be decreased (though not fully eliminated) by adaptation improvement of model parameter (e.g., through learning), and (2) additive uncertainties (a) (such as forecast uncertainty) that cannot be substantially decreased but that can be dealt with in the decision-making process.
In this Ph.D. proposal we aim to combining the strengths of MPC and reinforcement learning (RL) to continuously and in real time improve controller model parameters, objective function weights for conflicting objectives, controller hyperparameters, and so on. We will focus on forward uncertainty quantification. RL-MPC and stochastic MPCap models will be used. The sensitivity of these models will be analysed with respect to parameter choices which model the building. Similarly, a refinement/adjustment of how the control enters the mathematical model will be performed. The research will also investigate how to model outside uncertain disturbances like forecasts of weather and user behaviour.
The proposed solutions will be applied in optimal control with aim of achieving a preset thermal comfort level in buildings. In particular, we are interested in the improvement that can be achieved by temporal adjustment of the model parameters using a properly tuned method.
The PhD fellowship is granted within the collaboration between the University of Leuven and VITO.
The successful candidate will be supervised by Prof. Lieve Helsen (KU Leuven) and co-promoted by Dr. Brida Mbuwir (VITO).
For more information, please contact Dr. Brida Mbuwir: firstname.lastname@example.org.
How to apply?
Applications should be submitted online and should include a copy of your CV and a cover letter.
The remainder of the selection procedure is specific to the position and will be determined by the selection panel.
You can apply for this PhD vacancy no later than August 1, 2022.
More information is available on PhD | VITO.
- You hold a M.Sc. degree in mechanical engineering, electrical engineering, software engineering, computer science, mathematical engineering or data science.
- You are fluent in English, both oral and written.